{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 不参与建模的特征 ['os', 'sid’]\n",
    "## 添加了osv的使用\n",
    "## 添加version 和简单使用了timestamp\n",
    "## 使用sgb算法\n",
    "## 添加osv1对osv和version做一个差\n",
    "## 添加了lan的使用\n",
    "## 使用lgb的祖传参数\n",
    "## 添加屏幕比、屏幕面积、像素比\n",
    "## 5折lgb Score = 89.1873\n",
    "## 5折xgb Score = 89.108\n",
    "## 模型融合 Score = 89.1713"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
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       "        Unnamed: 0  android_id  apptype  carrier  dev_height  dev_ppi  \\\n",
       "0                0      316361     1199  46000.0         0.0      0.0   \n",
       "1                1      135939      893      0.0         0.0      0.0   \n",
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       "4                4      288999     1076  46000.0      2280.0      0.0   \n",
       "...            ...         ...      ...      ...         ...      ...   \n",
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       "\n",
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       "...           ...    ...    ...       ...  ...      ...    ...     ...   \n",
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       "\n",
       "            sid     timestamp  version    fea_hash location   fea1_hash  \\\n",
       "0       1438873  1.559893e+12        8  2135019403        0  2329670524   \n",
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       "...         ...           ...      ...         ...      ...         ...   \n",
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       "499996  1480612  1.559814e+12        3  1714444511       23  2745131047   \n",
       "499997  1698442  1.559676e+12        0  3843262581       25  1326115882   \n",
       "499998  1331155  1.559840e+12        0  1984296118      225  1446741112   \n",
       "499999  1373973  1.559922e+12        5  1697301943       49  1915763579   \n",
       "\n",
       "        cus_type  \n",
       "0            601  \n",
       "1           1000  \n",
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       "4            582  \n",
       "...          ...  \n",
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       "499996       525  \n",
       "499997       810  \n",
       "499998       772  \n",
       "499999      1076  \n",
       "\n",
       "[500000 rows x 21 columns]"
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     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import warnings\n",
    "from sklearn.model_selection import StratifiedKFold,KFold\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "\n",
    "import xgboost as xgb\n",
    "import lightgbm as lgb\n",
    "import pickle\n",
    "import numpy as np\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# 数据加载\n",
    "train = pd.read_csv('./train.csv')\n",
    "test = pd.read_csv('./test.csv')\n",
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "      <th>3</th>\n",
       "      <td>68983</td>\n",
       "      <td>1004</td>\n",
       "      <td>46000.0</td>\n",
       "      <td>2214.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1080.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>129</td>\n",
       "      <td>2.0</td>\n",
       "      <td>android</td>\n",
       "      <td>8.1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1093419</td>\n",
       "      <td>1.560042e+12</td>\n",
       "      <td>0</td>\n",
       "      <td>3562553457</td>\n",
       "      <td>3</td>\n",
       "      <td>1283809327</td>\n",
       "      <td>753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>288999</td>\n",
       "      <td>1076</td>\n",
       "      <td>46000.0</td>\n",
       "      <td>2280.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1080.0</td>\n",
       "      <td>1</td>\n",
       "      <td>zh-CN</td>\n",
       "      <td>64</td>\n",
       "      <td>2.0</td>\n",
       "      <td>android</td>\n",
       "      <td>8.0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1400089</td>\n",
       "      <td>1.559867e+12</td>\n",
       "      <td>5</td>\n",
       "      <td>2364522023</td>\n",
       "      <td>4</td>\n",
       "      <td>1510695983</td>\n",
       "      <td>582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499995</th>\n",
       "      <td>392477</td>\n",
       "      <td>1028</td>\n",
       "      <td>46000.0</td>\n",
       "      <td>1920.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1080.0</td>\n",
       "      <td>1</td>\n",
       "      <td>zh-CN</td>\n",
       "      <td>144</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Android</td>\n",
       "      <td>7.1.2</td>\n",
       "      <td>25</td>\n",
       "      <td>1546078</td>\n",
       "      <td>1.559834e+12</td>\n",
       "      <td>7</td>\n",
       "      <td>861755946</td>\n",
       "      <td>79</td>\n",
       "      <td>140647032</td>\n",
       "      <td>373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499996</th>\n",
       "      <td>346134</td>\n",
       "      <td>1001</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1424.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>720.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29</td>\n",
       "      <td>2.0</td>\n",
       "      <td>android</td>\n",
       "      <td>8.1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1480612</td>\n",
       "      <td>1.559814e+12</td>\n",
       "      <td>3</td>\n",
       "      <td>1714444511</td>\n",
       "      <td>23</td>\n",
       "      <td>2745131047</td>\n",
       "      <td>525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499997</th>\n",
       "      <td>499635</td>\n",
       "      <td>761</td>\n",
       "      <td>46000.0</td>\n",
       "      <td>1280.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>720.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54</td>\n",
       "      <td>6.0</td>\n",
       "      <td>android</td>\n",
       "      <td>6.0.1</td>\n",
       "      <td>9</td>\n",
       "      <td>1698442</td>\n",
       "      <td>1.559676e+12</td>\n",
       "      <td>0</td>\n",
       "      <td>3843262581</td>\n",
       "      <td>25</td>\n",
       "      <td>1326115882</td>\n",
       "      <td>810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499998</th>\n",
       "      <td>239786</td>\n",
       "      <td>917</td>\n",
       "      <td>46001.0</td>\n",
       "      <td>960.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>540.0</td>\n",
       "      <td>0</td>\n",
       "      <td>zh_CN</td>\n",
       "      <td>109</td>\n",
       "      <td>2.0</td>\n",
       "      <td>android</td>\n",
       "      <td>5.1.1</td>\n",
       "      <td>0</td>\n",
       "      <td>1331155</td>\n",
       "      <td>1.559840e+12</td>\n",
       "      <td>0</td>\n",
       "      <td>1984296118</td>\n",
       "      <td>225</td>\n",
       "      <td>1446741112</td>\n",
       "      <td>772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499999</th>\n",
       "      <td>270531</td>\n",
       "      <td>929</td>\n",
       "      <td>46000.0</td>\n",
       "      <td>2040.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1080.0</td>\n",
       "      <td>1</td>\n",
       "      <td>zh-CN</td>\n",
       "      <td>59</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Android</td>\n",
       "      <td>8.1.0</td>\n",
       "      <td>78</td>\n",
       "      <td>1373973</td>\n",
       "      <td>1.559922e+12</td>\n",
       "      <td>5</td>\n",
       "      <td>1697301943</td>\n",
       "      <td>49</td>\n",
       "      <td>1915763579</td>\n",
       "      <td>1076</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500000 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        android_id  apptype  carrier  dev_height  dev_ppi  dev_width  label  \\\n",
       "0           316361     1199  46000.0         0.0      0.0        0.0      1   \n",
       "1           135939      893      0.0         0.0      0.0        0.0      1   \n",
       "2           399254      821      0.0       760.0      0.0      360.0      1   \n",
       "3            68983     1004  46000.0      2214.0      0.0     1080.0      0   \n",
       "4           288999     1076  46000.0      2280.0      0.0     1080.0      1   \n",
       "...            ...      ...      ...         ...      ...        ...    ...   \n",
       "499995      392477     1028  46000.0      1920.0      3.0     1080.0      1   \n",
       "499996      346134     1001      0.0      1424.0      0.0      720.0      0   \n",
       "499997      499635      761  46000.0      1280.0      0.0      720.0      0   \n",
       "499998      239786      917  46001.0       960.0      0.0      540.0      0   \n",
       "499999      270531      929  46000.0      2040.0      3.0     1080.0      1   \n",
       "\n",
       "          lan  media_id  ntt       os    osv  package      sid     timestamp  \\\n",
       "0         NaN       104  6.0  android      9       18  1438873  1.559893e+12   \n",
       "1         NaN        19  6.0  android    8.1        0  1185582  1.559994e+12   \n",
       "2         NaN       559  0.0  android  8.1.0        0  1555716  1.559837e+12   \n",
       "3         NaN       129  2.0  android  8.1.0        0  1093419  1.560042e+12   \n",
       "4       zh-CN        64  2.0  android  8.0.0        0  1400089  1.559867e+12   \n",
       "...       ...       ...  ...      ...    ...      ...      ...           ...   \n",
       "499995  zh-CN       144  6.0  Android  7.1.2       25  1546078  1.559834e+12   \n",
       "499996    NaN        29  2.0  android  8.1.0        0  1480612  1.559814e+12   \n",
       "499997    NaN        54  6.0  android  6.0.1        9  1698442  1.559676e+12   \n",
       "499998  zh_CN       109  2.0  android  5.1.1        0  1331155  1.559840e+12   \n",
       "499999  zh-CN        59  2.0  Android  8.1.0       78  1373973  1.559922e+12   \n",
       "\n",
       "       version    fea_hash  location   fea1_hash  cus_type  \n",
       "0            8  2135019403         0  2329670524       601  \n",
       "1            4  2782306428         1  2864801071      1000  \n",
       "2            0  1392806005         2   628911675       696  \n",
       "3            0  3562553457         3  1283809327       753  \n",
       "4            5  2364522023         4  1510695983       582  \n",
       "...        ...         ...       ...         ...       ...  \n",
       "499995       7   861755946        79   140647032       373  \n",
       "499996       3  1714444511        23  2745131047       525  \n",
       "499997       0  3843262581        25  1326115882       810  \n",
       "499998       0  1984296118       225  1446741112       772  \n",
       "499999       5  1697301943        49  1915763579      1076  \n",
       "\n",
       "[500000 rows x 20 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = test.iloc[:, 1:]\n",
    "train = train.iloc[:, 1:]\n",
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "lan    183280\n",
       "osv      6561\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#train.info()\n",
    "#train['lan'].value_counts()\n",
    "# Object类型： lan, os, osv, version, fea_hash\n",
    "# 字符串类型 需要转换为数值（labelencoder）\n",
    "object_cols = train.select_dtypes(include='object').columns\n",
    "\n",
    "# 缺失值个数\n",
    "temp = train.isnull().sum()\n",
    "# 有缺失值的字段： lan, osv\n",
    "temp[temp>0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Object类型： lan, os, osv, version, fea_hash\n",
    "##### 有缺失值的字段： lan, osv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['android_id', 'apptype', 'carrier', 'dev_height', 'dev_ppi', 'dev_width', 'lan', 'media_id', 'ntt', 'os', 'osv', 'package', 'sid', 'timestamp', 'version', 'fea_hash', 'location', 'fea1_hash', 'cus_type']\n"
     ]
    }
   ],
   "source": [
    "# ['os', 'osv', 'lan', 'sid’]\n",
    "features = train.columns.tolist()\n",
    "features.remove('label')\n",
    "print(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "android_id 362258\n",
      "apptype 89\n",
      "carrier 5\n",
      "dev_height 798\n",
      "dev_ppi 92\n",
      "dev_width 346\n",
      "lan 21\n",
      "media_id 284\n",
      "ntt 8\n",
      "os 2\n",
      "osv 154\n",
      "package 1950\n",
      "sid 500000\n",
      "timestamp 500000\n",
      "version 22\n",
      "fea_hash 402980\n",
      "location 332\n",
      "fea1_hash 4959\n",
      "cus_type 58\n"
     ]
    }
   ],
   "source": [
    "for feature in features:\n",
    "    print(feature, train[feature].nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10    378925\n",
       "9     108904\n",
       "8      11235\n",
       "7        740\n",
       "6         93\n",
       "38        37\n",
       "39        28\n",
       "37        16\n",
       "5         11\n",
       "36         3\n",
       "1          2\n",
       "32         2\n",
       "33         2\n",
       "30         1\n",
       "31         1\n",
       "Name: fea_hash, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Thinking: fea_hash是否要做特征变换？\n",
    "#train['fea_hash'].value_counts()\n",
    "#train['fea_hash'].describe()\n",
    "train['fea_hash'].map(lambda x: len(str(x))).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10    391669\n",
       "9      99347\n",
       "8       8977\n",
       "7          6\n",
       "5          1\n",
       "Name: fea1_hash, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#train['fea1_hash'].value_counts()\n",
    "train['fea1_hash'].map(lambda x: len(str(x))).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 处理osv\n",
    "def trans_osv(osv):\n",
    "    global result\n",
    "    osv = str(osv).replace(' ','').replace('.','').replace('Android_','').replace('十核20G_HD','').replace('Android','').replace('W','')\n",
    "    if osv == 'nan' or osv == 'GIONEE_YNGA':\n",
    "        result = 810\n",
    "    elif osv.count('-') >0:\n",
    "        result = int(osv.split('-')[0])\n",
    "    elif osv == 'f073b_changxiang_v01_b1b8_20180915':\n",
    "        result = 810\n",
    "    elif osv == '%E6%B1%9F%E7%81%B5OS+50':\n",
    "        result = 500\n",
    "    else:\n",
    "        result = int(osv)\n",
    "        \n",
    "    if result < 10:\n",
    "        result = result * 100\n",
    "    elif  result < 100:\n",
    "        result = result * 10\n",
    "        \n",
    "    return int(result)\n",
    "\n",
    "# train['osv_trans'] = train['osv'].apply(trans_osv)\n",
    "# train['osv_trans']\n",
    "# train['osv'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 15, 20]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 转化app版本号\n",
    "def trans_version(version):\n",
    "    version = version.replace('V','').replace('P_Final_','').replace(' ','').replace('GA','').replace('v','')\n",
    "    if version == '50':\n",
    "        return int(5)\n",
    "    return int(version)\n",
    "test['version_trans'] = test['version'].apply(trans_version)\n",
    "list(set(test['version_trans']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 对语言进行文字化处理\n",
    "lan_dict = {'zh_CN':1,'zh-CN':2,'zh-cn':3,'Zh-CN':4,'ZH':5,'zh':6,'cn':7,'CN':8,'zh_CN_#Hans':9,'zh-HK':10,'zh-MO':11,'zh-TW':12,'tw':13,'TW':14,'en':15,'en-GB':16,'en-US':17,'ja':18,'mi':19,'ko':20,'it':21,'nan':22}\n",
    "train['lan'] = train['lan'].map(lan_dict) \n",
    "train['lan'] = train['lan'].fillna(22)\n",
    "\n",
    "test['lan'] = test['lan'].map(lan_dict) \n",
    "test['lan'] = test['lan'].fillna(22)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['android_id',\n",
       " 'apptype',\n",
       " 'carrier',\n",
       " 'dev_height',\n",
       " 'dev_ppi',\n",
       " 'dev_width',\n",
       " 'lan',\n",
       " 'media_id',\n",
       " 'ntt',\n",
       " 'osv',\n",
       " 'package',\n",
       " 'timestamp',\n",
       " 'version',\n",
       " 'fea_hash',\n",
       " 'location',\n",
       " 'fea1_hash',\n",
       " 'cus_type']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "remove_list = ['os', 'sid']\n",
    "col = features\n",
    "for i in remove_list:\n",
    "    col.remove(i)\n",
    "col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "# 特征筛选\n",
    "features = train[col]\n",
    "# 构造fea_hash_len特征\n",
    "features['fea_hash_len'] = features['fea_hash'].map(lambda x: len(str(x)))\n",
    "features['fea1_hash_len'] = features['fea1_hash'].map(lambda x: len(str(x)))\n",
    "# Thinking：为什么将很大的，很长的fea_hash化为0？\n",
    "# 如果fea_hash很长，都归为0，否则为自己的本身\n",
    "features['fea_hash'] = features['fea_hash'].map(lambda x: 0 if len(str(x))>16 else int(x))\n",
    "features['fea1_hash'] = features['fea1_hash'].map(lambda x: 0 if len(str(x))>16 else int(x))\n",
    "\n",
    "\n",
    "features['osv'] = features['osv'].apply(trans_osv)\n",
    "features['version'] = features['version'].apply(trans_version)\n",
    "\n",
    "features['timestamp'] = features['timestamp'].apply(lambda x : datetime.fromtimestamp(x/1000))\n",
    "# 分解时间\n",
    "features['year'] = features['timestamp'].dt.year\n",
    "features['month'] = features['timestamp'].dt.month\n",
    "features['day'] = features['timestamp'].dt.day\n",
    "features['weekday'] = features['timestamp'].dt.weekday\n",
    "features['hour'] = features['timestamp'].dt.hour\n",
    "features['minute'] = features['timestamp'].dt.minute\n",
    "# 获取time_diff\n",
    "start_time = features['timestamp'].min()\n",
    "features['timestamp_diff'] = features['timestamp']-start_time\n",
    "features['timestamp_diff'] = features['timestamp_diff'].dt.days - features['timestamp_diff'].dt.seconds/3600/24\n",
    "\n",
    "# 对版本进行处理\n",
    "features['osv1'] = features['osv'] - features['version'] \n",
    "\n",
    "# # 设别高宽比、设备面积\n",
    "features['dev_rate'] = features['dev_height']/features['dev_width']\n",
    "features['dev_area'] = features['dev_height']*features['dev_width']\n",
    "\n",
    "# # 面积与分辨率比\n",
    "features['dev_area_ppi'] = features['dev_ppi']/features['dev_area']\n",
    "\n",
    "features['dev_area_ppi'][np.isinf(features['dev_area_ppi'])] = 0\n",
    "\n",
    "\n",
    "features.fillna(-1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_features = test[col]\n",
    "# 构造fea_hash_len特征\n",
    "test_features['fea_hash_len'] = test_features['fea_hash'].map(lambda x: len(str(x)))\n",
    "test_features['fea1_hash_len'] = test_features['fea1_hash'].map(lambda x: len(str(x)))\n",
    "# Thinking：为什么将很大的，很长的fea_hash化为0？\n",
    "# 如果fea_hash很长，都归为0，否则为自己的本身\n",
    "test_features['fea_hash'] = test_features['fea_hash'].map(lambda x: 0 if len(str(x))>16 else int(x))\n",
    "test_features['fea1_hash'] = test_features['fea1_hash'].map(lambda x: 0 if len(str(x))>16 else int(x))\n",
    "\n",
    "test_features['osv'] = test_features['osv'].apply(trans_osv)\n",
    "test_features['version'] = test_features['version'].apply(trans_version)\n",
    "\n",
    "test_features['timestamp'] = test_features['timestamp'].apply(lambda x : datetime.fromtimestamp(x/1000))\n",
    "# 分解时间\n",
    "test_features['year'] = test_features['timestamp'].dt.year\n",
    "test_features['month'] = test_features['timestamp'].dt.month\n",
    "test_features['day'] = test_features['timestamp'].dt.day\n",
    "test_features['weekday'] = test_features['timestamp'].dt.weekday\n",
    "test_features['hour'] = test_features['timestamp'].dt.hour\n",
    "test_features['minute'] = test_features['timestamp'].dt.minute\n",
    "# 获取time_diff\n",
    "start_time = features['timestamp'].min()\n",
    "test_features['timestamp_diff'] = test_features['timestamp']-start_time\n",
    "test_features['timestamp_diff'] = test_features['timestamp_diff'].dt.days - test_features['timestamp_diff'].dt.seconds/3600/24\n",
    "\n",
    "# test_features['lan'] = test_features['lan'].apply(trans_lan)\n",
    "test_features['osv1'] = test_features['osv'] - test_features['version'] \n",
    "\n",
    "# 设别高宽比、设备面积\n",
    "test_features['dev_rate'] = test_features['dev_height']/test_features['dev_width']\n",
    "test_features['dev_area'] = test_features['dev_height']*test_features['dev_width']\n",
    "\n",
    "# 面积与分辨率比\n",
    "test_features['dev_area_ppi'] = test_features['dev_ppi']/test_features['dev_area']\n",
    "\n",
    "test_features['dev_area_ppi'][np.isinf(test_features['dev_area_ppi'])] = 0\n",
    "\n",
    "\n",
    "test_features.fillna(-1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "训练\n",
      "[LightGBM] [Warning] feature_fraction is set=0.8, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=0.8 will be ignored. Current value: bagging_fraction=0.8\n",
      "[1]\tvalid_0's auc: 0.930697\n",
      "[2]\tvalid_0's auc: 0.932603\n",
      "[3]\tvalid_0's auc: 0.933029\n",
      "[4]\tvalid_0's auc: 0.932838\n",
      "[5]\tvalid_0's auc: 0.933771\n",
      "[6]\tvalid_0's auc: 0.934393\n",
      "[7]\tvalid_0's auc: 0.934437\n",
      "[8]\tvalid_0's auc: 0.934355\n",
      "[9]\tvalid_0's auc: 0.934325\n",
      "[10]\tvalid_0's auc: 0.934521\n",
      "[11]\tvalid_0's auc: 0.934557\n",
      "[12]\tvalid_0's auc: 0.934566\n",
      "[13]\tvalid_0's auc: 0.934527\n",
      "[14]\tvalid_0's auc: 0.934647\n",
      "[15]\tvalid_0's auc: 0.935076\n",
      "[16]\tvalid_0's auc: 0.935473\n",
      "[17]\tvalid_0's auc: 0.935492\n",
      "[18]\tvalid_0's auc: 0.935422\n",
      "[19]\tvalid_0's auc: 0.935398\n",
      "[20]\tvalid_0's auc: 0.935383\n",
      "[21]\tvalid_0's auc: 0.935368\n",
      "[22]\tvalid_0's auc: 0.935626\n",
      "[23]\tvalid_0's auc: 0.9356\n",
      "[24]\tvalid_0's auc: 0.935533\n",
      "[25]\tvalid_0's auc: 0.935456\n",
      "[26]\tvalid_0's auc: 0.935506\n",
      "[27]\tvalid_0's auc: 0.93577\n",
      "[28]\tvalid_0's auc: 0.935737\n",
      "[29]\tvalid_0's auc: 0.935713\n",
      "[30]\tvalid_0's auc: 0.935678\n",
      "[31]\tvalid_0's auc: 0.935693\n",
      "[32]\tvalid_0's auc: 0.935663\n",
      "[33]\tvalid_0's auc: 0.935626\n",
      "[34]\tvalid_0's auc: 0.935821\n",
      "[35]\tvalid_0's auc: 0.936054\n",
      "[36]\tvalid_0's auc: 0.936059\n",
      "[37]\tvalid_0's auc: 0.935999\n",
      "[38]\tvalid_0's auc: 0.936004\n",
      "[39]\tvalid_0's auc: 0.936086\n",
      "[40]\tvalid_0's auc: 0.936101\n",
      "[41]\tvalid_0's auc: 0.93613\n",
      "[42]\tvalid_0's auc: 0.936167\n",
      "[43]\tvalid_0's auc: 0.936129\n",
      "[44]\tvalid_0's auc: 0.936134\n",
      "[45]\tvalid_0's auc: 0.936174\n",
      "[46]\tvalid_0's auc: 0.936156\n",
      "[47]\tvalid_0's auc: 0.936168\n",
      "[48]\tvalid_0's auc: 0.936154\n",
      "[49]\tvalid_0's auc: 0.936165\n",
      "[50]\tvalid_0's auc: 0.936147\n",
      "[51]\tvalid_0's auc: 0.936168\n",
      "[52]\tvalid_0's auc: 0.93616\n",
      "[53]\tvalid_0's auc: 0.936159\n",
      "[54]\tvalid_0's auc: 0.936162\n",
      "[55]\tvalid_0's auc: 0.936146\n",
      "[56]\tvalid_0's auc: 0.936186\n",
      "[57]\tvalid_0's auc: 0.936187\n",
      "[58]\tvalid_0's auc: 0.936209\n",
      "[59]\tvalid_0's auc: 0.93622\n",
      "[60]\tvalid_0's auc: 0.936193\n",
      "[61]\tvalid_0's auc: 0.936228\n",
      "[62]\tvalid_0's auc: 0.936235\n",
      "[63]\tvalid_0's auc: 0.936232\n",
      "[64]\tvalid_0's auc: 0.936232\n",
      "[65]\tvalid_0's auc: 0.936236\n",
      "[66]\tvalid_0's auc: 0.936226\n",
      "[67]\tvalid_0's auc: 0.936207\n",
      "[68]\tvalid_0's auc: 0.936199\n",
      "[69]\tvalid_0's auc: 0.936196\n",
      "[70]\tvalid_0's auc: 0.936197\n",
      "[71]\tvalid_0's auc: 0.936284\n",
      "[72]\tvalid_0's auc: 0.936326\n",
      "[73]\tvalid_0's auc: 0.936327\n",
      "[74]\tvalid_0's auc: 0.93633\n",
      "[75]\tvalid_0's auc: 0.936343\n",
      "[76]\tvalid_0's auc: 0.936445\n",
      "[77]\tvalid_0's auc: 0.936445\n",
      "[78]\tvalid_0's auc: 0.936459\n",
      "[79]\tvalid_0's auc: 0.936434\n",
      "[80]\tvalid_0's auc: 0.936522\n",
      "[81]\tvalid_0's auc: 0.936534\n",
      "[82]\tvalid_0's auc: 0.936529\n",
      "[83]\tvalid_0's auc: 0.93653\n",
      "[84]\tvalid_0's auc: 0.936535\n",
      "[85]\tvalid_0's auc: 0.936497\n",
      "[86]\tvalid_0's auc: 0.936496\n",
      "[87]\tvalid_0's auc: 0.936518\n",
      "[88]\tvalid_0's auc: 0.936538\n",
      "[89]\tvalid_0's auc: 0.936582\n",
      "[90]\tvalid_0's auc: 0.936591\n",
      "[91]\tvalid_0's auc: 0.936583\n",
      "[92]\tvalid_0's auc: 0.936672\n",
      "[93]\tvalid_0's auc: 0.936684\n",
      "[94]\tvalid_0's auc: 0.936669\n",
      "[95]\tvalid_0's auc: 0.936645\n",
      "[96]\tvalid_0's auc: 0.936639\n",
      "[97]\tvalid_0's auc: 0.936717\n",
      "[98]\tvalid_0's auc: 0.936768\n",
      "[99]\tvalid_0's auc: 0.936756\n",
      "[100]\tvalid_0's auc: 0.936763\n",
      "[101]\tvalid_0's auc: 0.936777\n",
      "[102]\tvalid_0's auc: 0.936792\n",
      "[103]\tvalid_0's auc: 0.936859\n",
      "[104]\tvalid_0's auc: 0.936908\n",
      "[105]\tvalid_0's auc: 0.936889\n",
      "[106]\tvalid_0's auc: 0.936903\n",
      "[107]\tvalid_0's auc: 0.93695\n",
      "[108]\tvalid_0's auc: 0.936952\n",
      "[109]\tvalid_0's auc: 0.936949\n",
      "[110]\tvalid_0's auc: 0.936939\n",
      "[111]\tvalid_0's auc: 0.936941\n",
      "[112]\tvalid_0's auc: 0.936998\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-14-5b6b6b29a361>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     28\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'训练'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     29\u001b[0m     \u001b[1;31m# 训练\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 30\u001b[1;33m     model = model.fit(X_train,\n\u001b[0m\u001b[0;32m     31\u001b[0m                           \u001b[0my_train\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     32\u001b[0m                           \u001b[0meval_set\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_val\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_val\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\env\\Anaconda3\\lib\\site-packages\\lightgbm\\sklearn.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight, init_score, eval_set, eval_names, eval_sample_weight, eval_class_weight, eval_init_score, eval_metric, early_stopping_rounds, verbose, feature_name, categorical_feature, callbacks, init_model)\u001b[0m\n\u001b[0;32m    965\u001b[0m                     \u001b[0mvalid_sets\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mvalid_x\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_le\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalid_y\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    966\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 967\u001b[1;33m         super().fit(X, _y, sample_weight=sample_weight, init_score=init_score, eval_set=valid_sets,\n\u001b[0m\u001b[0;32m    968\u001b[0m                     \u001b[0meval_names\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0meval_names\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0meval_sample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0meval_sample_weight\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    969\u001b[0m                     \u001b[0meval_class_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0meval_class_weight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0meval_init_score\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0meval_init_score\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\env\\Anaconda3\\lib\\site-packages\\lightgbm\\sklearn.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight, init_score, group, eval_set, eval_names, eval_sample_weight, eval_class_weight, eval_init_score, eval_group, eval_metric, early_stopping_rounds, verbose, feature_name, categorical_feature, callbacks, init_model)\u001b[0m\n\u001b[0;32m    746\u001b[0m         \u001b[0mcallbacks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrecord_evaluation\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mevals_result\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    747\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 748\u001b[1;33m         self._Booster = train(\n\u001b[0m\u001b[0;32m    749\u001b[0m             \u001b[0mparams\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    750\u001b[0m             \u001b[0mtrain_set\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtrain_set\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\env\\Anaconda3\\lib\\site-packages\\lightgbm\\engine.py\u001b[0m in \u001b[0;36mtrain\u001b[1;34m(params, train_set, num_boost_round, valid_sets, valid_names, fobj, feval, init_model, feature_name, categorical_feature, early_stopping_rounds, evals_result, verbose_eval, learning_rates, keep_training_booster, callbacks)\u001b[0m\n\u001b[0;32m    290\u001b[0m                                     evaluation_result_list=None))\n\u001b[0;32m    291\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 292\u001b[1;33m         \u001b[0mbooster\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfobj\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    293\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    294\u001b[0m         \u001b[0mevaluation_result_list\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\env\\Anaconda3\\lib\\site-packages\\lightgbm\\basic.py\u001b[0m in \u001b[0;36mupdate\u001b[1;34m(self, train_set, fobj)\u001b[0m\n\u001b[0;32m   3019\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__set_objective_to_none\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3020\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0mLightGBMError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Cannot update due to null objective function.'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3021\u001b[1;33m             _safe_call(_LIB.LGBM_BoosterUpdateOneIter(\n\u001b[0m\u001b[0;32m   3022\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3023\u001b[0m                 ctypes.byref(is_finished)))\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "\n",
    "seed = 2\n",
    "# model = xgb.XGBClassifier(tree_method='gpu_hist',n_estimators=200,reg_alpha=0.01)\n",
    "model = lgb.LGBMClassifier(num_leaves=512,\n",
    "                           max_depth=10,\n",
    "                           learning_rate=0.005,\n",
    "                           n_estimators=5000,\n",
    "                           subsample=0.8,\n",
    "                           feature_fraction=0.8,\n",
    "                           reg_alpha=0.5,\n",
    "                           reg_lambda=0.5,\n",
    "                           random_state=seed,\n",
    "                           metric='auc',\n",
    "                           boosting_type='gbdt',\n",
    "                           subsample_freq=1,\n",
    "                           bagging_fraction=0.8)\n",
    "\n",
    "\n",
    "X = features.drop(['timestamp'], axis=1)\n",
    "y = train['label']\n",
    "\n",
    "skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=2019)\n",
    "prob = []\n",
    "mean_acc = 0\n",
    "for k,(train_index, test_index) in enumerate(skf.split(X, y)):\n",
    "    print(k)\n",
    "    X_train, X_val = X.iloc[train_index], X.iloc[test_index]\n",
    "    y_train, y_val = y.iloc[train_index], y.iloc[test_index]\n",
    "    print('训练')\n",
    "    # 训练\n",
    "    model = model.fit(X_train,\n",
    "                          y_train,\n",
    "                          eval_set=[(X_val, y_val)],\n",
    "                          eval_metric='auc',\n",
    "                          verbose = True)\n",
    "    # 正式预测\n",
    "    test_y_pred = model.predict_proba(test_features.drop(['timestamp'], axis=1))\n",
    "    prob.append(test_y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "训练\n",
      "[14:16:34] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:576: \n",
      "Parameters: { \"min_child_samples\" } might not be used.\n",
      "\n",
      "  This could be a false alarm, with some parameters getting used by language bindings but\n",
      "  then being mistakenly passed down to XGBoost core, or some parameter actually being used\n",
      "  but getting flagged wrongly here. Please open an issue if you find any such cases.\n",
      "\n",
      "\n",
      "[0]\tvalidation_0-auc:0.93503\n",
      "[1]\tvalidation_0-auc:0.93893\n",
      "[2]\tvalidation_0-auc:0.94039\n",
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      "第1个子模型 accuracy0.89027\n",
      "1\n",
      "训练\n",
      "[14:35:29] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:576: \n",
      "Parameters: { \"min_child_samples\" } might not be used.\n",
      "\n",
      "  This could be a false alarm, with some parameters getting used by language bindings but\n",
      "  then being mistakenly passed down to XGBoost core, or some parameter actually being used\n",
      "  but getting flagged wrongly here. Please open an issue if you find any such cases.\n",
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    {
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      "第2个子模型 accuracy0.89134\n",
      "2\n",
      "训练\n",
      "[14:53:53] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:576: \n",
      "Parameters: { \"min_child_samples\" } might not be used.\n",
      "\n",
      "  This could be a false alarm, with some parameters getting used by language bindings but\n",
      "  then being mistakenly passed down to XGBoost core, or some parameter actually being used\n",
      "  but getting flagged wrongly here. Please open an issue if you find any such cases.\n",
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      "第3个子模型 accuracy0.89189\n",
      "3\n",
      "训练\n",
      "[15:12:57] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:576: \n",
      "Parameters: { \"min_child_samples\" } might not be used.\n",
      "\n",
      "  This could be a false alarm, with some parameters getting used by language bindings but\n",
      "  then being mistakenly passed down to XGBoost core, or some parameter actually being used\n",
      "  but getting flagged wrongly here. Please open an issue if you find any such cases.\n",
      "\n",
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    {
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    {
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      "第4个子模型 accuracy0.89257\n",
      "4\n",
      "训练\n",
      "[15:32:18] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:576: \n",
      "Parameters: { \"min_child_samples\" } might not be used.\n",
      "\n",
      "  This could be a false alarm, with some parameters getting used by language bindings but\n",
      "  then being mistakenly passed down to XGBoost core, or some parameter actually being used\n",
      "  but getting flagged wrongly here. Please open an issue if you find any such cases.\n",
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      "[2820]\tvalidation_0-auc:0.94316\n",
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      "[2833]\tvalidation_0-auc:0.94315\n",
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      "[2875]\tvalidation_0-auc:0.94314\n",
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      "[2906]\tvalidation_0-auc:0.94314\n",
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      "[2956]\tvalidation_0-auc:0.94314\n",
      "[2957]\tvalidation_0-auc:0.94313\n",
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      "[2959]\tvalidation_0-auc:0.94313\n",
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      "[2990]\tvalidation_0-auc:0.94313\n",
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      "[2992]\tvalidation_0-auc:0.94313\n",
      "[2993]\tvalidation_0-auc:0.94313\n",
      "[2994]\tvalidation_0-auc:0.94313\n",
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      "[2996]\tvalidation_0-auc:0.94313\n",
      "[2997]\tvalidation_0-auc:0.94313\n",
      "[2998]\tvalidation_0-auc:0.94313\n",
      "[2999]\tvalidation_0-auc:0.94314\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第5个子模型 accuracy0.89016\n"
     ]
    }
   ],
   "source": [
    "import xgboost as xgb\n",
    "X = features.drop(['timestamp'], axis=1)\n",
    "y = train['label']\n",
    "xgb_model = xgb.XGBClassifier(\n",
    "            max_depth=15, learning_rate=0.005, n_estimators=3000, \n",
    "            objective='binary:logistic', tree_method='auto', \n",
    "            subsample=0.7, colsample_bytree=0.7, \n",
    "            min_child_samples=3, eval_metric='auc', reg_lambda=0.5\n",
    "        )\n",
    "skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=2019)\n",
    "xgb_prob = []\n",
    "xgb_mean_acc = 0\n",
    "for k,(train_index, test_index) in enumerate(skf.split(X, y)):\n",
    "    print(k)\n",
    "    X_train, X_val = X.iloc[train_index], X.iloc[test_index]\n",
    "    y_train, y_val = y.iloc[train_index], y.iloc[test_index]\n",
    "    print('训练')\n",
    "    # 训练\n",
    "    xgb = xgb_model.fit(X_train, y_train,\n",
    "                        eval_set=[(X_val, y_val)],\n",
    "                          eval_metric='auc',\n",
    "                          verbose = True)\n",
    "    \n",
    "    \n",
    "    val_y_pred=xgb_model.predict(X_val)\n",
    "    acc_val=accuracy_score(y_val,val_y_pred)\n",
    "    print('第{}个子模型 accuracy{}'.format(k+1,acc_val))\n",
    "    # 正式预测\n",
    "    test_y_pred=xgb_model.predict_proba(test_features.drop(['timestamp'], axis=1))\n",
    "    xgb_prob.append(test_y_pred)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "训练\n",
      "0:\tlearn: 0.8651333\ttest: 0.8638000\tbest: 0.8638000 (0)\ttotal: 389ms\tremaining: 1h 4m 48s\n",
      "100:\tlearn: 0.8760089\ttest: 0.8749200\tbest: 0.8749200 (100)\ttotal: 38.1s\tremaining: 1h 2m 15s\n",
      "200:\tlearn: 0.8810978\ttest: 0.8798400\tbest: 0.8798400 (196)\ttotal: 1m 15s\tremaining: 1h 1m 41s\n",
      "300:\tlearn: 0.8837667\ttest: 0.8823000\tbest: 0.8823000 (300)\ttotal: 1m 54s\tremaining: 1h 1m 25s\n",
      "400:\tlearn: 0.8854333\ttest: 0.8840000\tbest: 0.8840000 (400)\ttotal: 2m 32s\tremaining: 1h 52s\n",
      "500:\tlearn: 0.8870667\ttest: 0.8855000\tbest: 0.8855000 (500)\ttotal: 3m 11s\tremaining: 1h 25s\n",
      "600:\tlearn: 0.8886022\ttest: 0.8872200\tbest: 0.8872400 (599)\ttotal: 3m 49s\tremaining: 59m 51s\n",
      "700:\tlearn: 0.8896533\ttest: 0.8882400\tbest: 0.8882400 (700)\ttotal: 4m 27s\tremaining: 59m 13s\n",
      "800:\tlearn: 0.8905533\ttest: 0.8886800\tbest: 0.8886800 (799)\ttotal: 5m 6s\tremaining: 58m 35s\n",
      "900:\tlearn: 0.8913600\ttest: 0.8890400\tbest: 0.8890400 (900)\ttotal: 5m 44s\tremaining: 57m 57s\n",
      "1000:\tlearn: 0.8924489\ttest: 0.8894200\tbest: 0.8894800 (998)\ttotal: 6m 23s\tremaining: 57m 27s\n",
      "1100:\tlearn: 0.8933556\ttest: 0.8896400\tbest: 0.8897600 (1085)\ttotal: 7m 2s\tremaining: 56m 53s\n",
      "1200:\tlearn: 0.8943778\ttest: 0.8895800\tbest: 0.8897600 (1085)\ttotal: 7m 41s\tremaining: 56m 18s\n",
      "1300:\tlearn: 0.8953800\ttest: 0.8895600\tbest: 0.8897600 (1085)\ttotal: 8m 19s\tremaining: 55m 41s\n",
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      "1500:\tlearn: 0.8978133\ttest: 0.8896800\tbest: 0.8898000 (1443)\ttotal: 9m 37s\tremaining: 54m 29s\n",
      "1600:\tlearn: 0.8991356\ttest: 0.8897800\tbest: 0.8898200 (1552)\ttotal: 10m 16s\tremaining: 53m 53s\n",
      "1700:\tlearn: 0.9003178\ttest: 0.8897200\tbest: 0.8899200 (1613)\ttotal: 10m 55s\tremaining: 53m 16s\n",
      "1800:\tlearn: 0.9013356\ttest: 0.8899800\tbest: 0.8899800 (1800)\ttotal: 11m 33s\tremaining: 52m 38s\n",
      "1900:\tlearn: 0.9026356\ttest: 0.8899000\tbest: 0.8900200 (1823)\ttotal: 12m 12s\tremaining: 52m 2s\n",
      "2000:\tlearn: 0.9038289\ttest: 0.8898200\tbest: 0.8900200 (1823)\ttotal: 12m 51s\tremaining: 51m 24s\n",
      "2100:\tlearn: 0.9049356\ttest: 0.8898200\tbest: 0.8900200 (1823)\ttotal: 13m 30s\tremaining: 50m 48s\n",
      "2200:\tlearn: 0.9060489\ttest: 0.8899400\tbest: 0.8900400 (2189)\ttotal: 14m 9s\tremaining: 50m 10s\n",
      "2300:\tlearn: 0.9071644\ttest: 0.8898600\tbest: 0.8900400 (2189)\ttotal: 14m 48s\tremaining: 49m 32s\n",
      "2400:\tlearn: 0.9083756\ttest: 0.8900200\tbest: 0.8900800 (2359)\ttotal: 15m 27s\tremaining: 48m 55s\n",
      "2500:\tlearn: 0.9094956\ttest: 0.8901200\tbest: 0.8901600 (2458)\ttotal: 16m 6s\tremaining: 48m 17s\n",
      "2600:\tlearn: 0.9106778\ttest: 0.8901000\tbest: 0.8901600 (2458)\ttotal: 16m 45s\tremaining: 47m 39s\n",
      "2700:\tlearn: 0.9117667\ttest: 0.8903200\tbest: 0.8903600 (2683)\ttotal: 17m 23s\tremaining: 47m\n",
      "2800:\tlearn: 0.9130067\ttest: 0.8901800\tbest: 0.8903600 (2683)\ttotal: 18m 2s\tremaining: 46m 23s\n",
      "2900:\tlearn: 0.9141067\ttest: 0.8900600\tbest: 0.8903600 (2683)\ttotal: 18m 41s\tremaining: 45m 45s\n",
      "3000:\tlearn: 0.9152089\ttest: 0.8903200\tbest: 0.8903600 (2683)\ttotal: 19m 21s\tremaining: 45m 8s\n",
      "3100:\tlearn: 0.9162756\ttest: 0.8902800\tbest: 0.8903800 (3025)\ttotal: 20m\tremaining: 44m 30s\n",
      "3200:\tlearn: 0.9173933\ttest: 0.8901000\tbest: 0.8904200 (3157)\ttotal: 20m 39s\tremaining: 43m 52s\n",
      "3300:\tlearn: 0.9186267\ttest: 0.8901200\tbest: 0.8904200 (3157)\ttotal: 21m 18s\tremaining: 43m 14s\n",
      "3400:\tlearn: 0.9198333\ttest: 0.8901400\tbest: 0.8904200 (3157)\ttotal: 21m 57s\tremaining: 42m 37s\n",
      "3500:\tlearn: 0.9210267\ttest: 0.8902000\tbest: 0.8904200 (3157)\ttotal: 22m 37s\tremaining: 41m 59s\n",
      "3600:\tlearn: 0.9221756\ttest: 0.8902200\tbest: 0.8904200 (3157)\ttotal: 23m 16s\tremaining: 41m 21s\n",
      "3700:\tlearn: 0.9233844\ttest: 0.8901600\tbest: 0.8904200 (3157)\ttotal: 23m 55s\tremaining: 40m 42s\n",
      "3800:\tlearn: 0.9244978\ttest: 0.8899400\tbest: 0.8904200 (3157)\ttotal: 24m 34s\tremaining: 40m 4s\n",
      "3900:\tlearn: 0.9255978\ttest: 0.8899600\tbest: 0.8904200 (3157)\ttotal: 25m 12s\tremaining: 39m 25s\n",
      "4000:\tlearn: 0.9267444\ttest: 0.8899200\tbest: 0.8904200 (3157)\ttotal: 25m 51s\tremaining: 38m 46s\n",
      "4100:\tlearn: 0.9279111\ttest: 0.8899400\tbest: 0.8904200 (3157)\ttotal: 26m 31s\tremaining: 38m 9s\n",
      "bestTest = 0.89042\n",
      "bestIteration = 3157\n",
      "Shrink model to first 3158 iterations.\n",
      "第1个子模型 accuracy0.89042\n",
      "1\n",
      "训练\n",
      "0:\tlearn: 0.8650911\ttest: 0.8617800\tbest: 0.8617800 (0)\ttotal: 382ms\tremaining: 1h 3m 40s\n",
      "100:\tlearn: 0.8767489\ttest: 0.8732600\tbest: 0.8735000 (86)\ttotal: 38.1s\tremaining: 1h 2m 13s\n",
      "200:\tlearn: 0.8811089\ttest: 0.8783000\tbest: 0.8787000 (194)\ttotal: 1m 15s\tremaining: 1h 1m 35s\n",
      "300:\tlearn: 0.8842511\ttest: 0.8806800\tbest: 0.8807000 (299)\ttotal: 1m 53s\tremaining: 1h 58s\n",
      "400:\tlearn: 0.8856022\ttest: 0.8818000\tbest: 0.8818400 (398)\ttotal: 2m 31s\tremaining: 1h 18s\n",
      "500:\tlearn: 0.8871600\ttest: 0.8834000\tbest: 0.8834000 (500)\ttotal: 3m 8s\tremaining: 59m 41s\n",
      "600:\tlearn: 0.8891756\ttest: 0.8848200\tbest: 0.8849400 (590)\ttotal: 3m 46s\tremaining: 59m 4s\n",
      "700:\tlearn: 0.8902156\ttest: 0.8853400\tbest: 0.8854400 (683)\ttotal: 4m 24s\tremaining: 58m 26s\n",
      "800:\tlearn: 0.8909644\ttest: 0.8857000\tbest: 0.8857000 (800)\ttotal: 5m 2s\tremaining: 57m 49s\n",
      "900:\tlearn: 0.8917422\ttest: 0.8858600\tbest: 0.8858800 (865)\ttotal: 5m 39s\tremaining: 57m 12s\n",
      "1000:\tlearn: 0.8927200\ttest: 0.8862400\tbest: 0.8862800 (992)\ttotal: 6m 17s\tremaining: 56m 37s\n",
      "1100:\tlearn: 0.8936556\ttest: 0.8865400\tbest: 0.8865600 (1095)\ttotal: 6m 56s\tremaining: 56m 3s\n",
      "1200:\tlearn: 0.8946667\ttest: 0.8866400\tbest: 0.8867600 (1163)\ttotal: 7m 34s\tremaining: 55m 28s\n",
      "1300:\tlearn: 0.8957822\ttest: 0.8867800\tbest: 0.8870000 (1256)\ttotal: 8m 12s\tremaining: 54m 53s\n",
      "1400:\tlearn: 0.8968822\ttest: 0.8869600\tbest: 0.8870200 (1333)\ttotal: 8m 50s\tremaining: 54m 18s\n",
      "1500:\tlearn: 0.8979311\ttest: 0.8871000\tbest: 0.8871600 (1493)\ttotal: 9m 29s\tremaining: 53m 43s\n",
      "1600:\tlearn: 0.8990467\ttest: 0.8871800\tbest: 0.8872600 (1590)\ttotal: 10m 7s\tremaining: 53m 8s\n",
      "1700:\tlearn: 0.9002556\ttest: 0.8873400\tbest: 0.8873400 (1698)\ttotal: 10m 46s\tremaining: 52m 33s\n",
      "1800:\tlearn: 0.9012756\ttest: 0.8872000\tbest: 0.8874000 (1719)\ttotal: 11m 24s\tremaining: 51m 57s\n",
      "1900:\tlearn: 0.9024844\ttest: 0.8872400\tbest: 0.8874000 (1719)\ttotal: 12m 3s\tremaining: 51m 22s\n",
      "2000:\tlearn: 0.9036756\ttest: 0.8869400\tbest: 0.8874000 (1719)\ttotal: 12m 42s\tremaining: 50m 46s\n",
      "2100:\tlearn: 0.9049378\ttest: 0.8868600\tbest: 0.8874000 (1719)\ttotal: 13m 21s\tremaining: 50m 12s\n",
      "2200:\tlearn: 0.9059667\ttest: 0.8871600\tbest: 0.8874000 (1719)\ttotal: 13m 59s\tremaining: 49m 35s\n",
      "2300:\tlearn: 0.9071356\ttest: 0.8872200\tbest: 0.8874000 (1719)\ttotal: 14m 38s\tremaining: 48m 59s\n",
      "2400:\tlearn: 0.9084822\ttest: 0.8873800\tbest: 0.8874400 (2393)\ttotal: 15m 17s\tremaining: 48m 22s\n",
      "2500:\tlearn: 0.9096222\ttest: 0.8872200\tbest: 0.8874400 (2393)\ttotal: 15m 55s\tremaining: 47m 45s\n",
      "2600:\tlearn: 0.9109311\ttest: 0.8874000\tbest: 0.8874600 (2586)\ttotal: 16m 34s\tremaining: 47m 9s\n",
      "2700:\tlearn: 0.9121089\ttest: 0.8874200\tbest: 0.8875400 (2696)\ttotal: 17m 13s\tremaining: 46m 32s\n",
      "2800:\tlearn: 0.9132156\ttest: 0.8872200\tbest: 0.8875400 (2696)\ttotal: 17m 51s\tremaining: 45m 55s\n",
      "2900:\tlearn: 0.9143333\ttest: 0.8872400\tbest: 0.8875400 (2696)\ttotal: 18m 30s\tremaining: 45m 18s\n",
      "3000:\tlearn: 0.9155600\ttest: 0.8873400\tbest: 0.8875400 (2696)\ttotal: 19m 9s\tremaining: 44m 41s\n",
      "3100:\tlearn: 0.9168044\ttest: 0.8872000\tbest: 0.8875400 (2696)\ttotal: 19m 48s\tremaining: 44m 3s\n",
      "3200:\tlearn: 0.9178911\ttest: 0.8872200\tbest: 0.8875400 (2696)\ttotal: 20m 27s\tremaining: 43m 26s\n",
      "3300:\tlearn: 0.9191444\ttest: 0.8871600\tbest: 0.8875400 (2696)\ttotal: 21m 5s\tremaining: 42m 49s\n",
      "3400:\tlearn: 0.9202444\ttest: 0.8871600\tbest: 0.8875400 (2696)\ttotal: 21m 44s\tremaining: 42m 11s\n",
      "3500:\tlearn: 0.9212689\ttest: 0.8870400\tbest: 0.8875400 (2696)\ttotal: 22m 23s\tremaining: 41m 34s\n",
      "3600:\tlearn: 0.9224467\ttest: 0.8869600\tbest: 0.8875400 (2696)\ttotal: 23m 2s\tremaining: 40m 56s\n",
      "bestTest = 0.88754\n",
      "bestIteration = 2696\n",
      "Shrink model to first 2697 iterations.\n",
      "第2个子模型 accuracy0.88754\n",
      "2\n",
      "训练\n",
      "0:\tlearn: 0.8615733\ttest: 0.8597400\tbest: 0.8597400 (0)\ttotal: 383ms\tremaining: 1h 3m 49s\n",
      "100:\tlearn: 0.8766489\ttest: 0.8747000\tbest: 0.8748200 (95)\ttotal: 38.2s\tremaining: 1h 2m 21s\n",
      "200:\tlearn: 0.8815000\ttest: 0.8782800\tbest: 0.8783200 (197)\ttotal: 1m 15s\tremaining: 1h 1m 40s\n",
      "300:\tlearn: 0.8840556\ttest: 0.8810000\tbest: 0.8810000 (299)\ttotal: 1m 53s\tremaining: 1h 1m 4s\n",
      "400:\tlearn: 0.8856533\ttest: 0.8824400\tbest: 0.8824600 (399)\ttotal: 2m 31s\tremaining: 1h 23s\n",
      "500:\tlearn: 0.8873889\ttest: 0.8834200\tbest: 0.8834400 (495)\ttotal: 3m 9s\tremaining: 59m 45s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "600:\tlearn: 0.8890644\ttest: 0.8845000\tbest: 0.8845000 (600)\ttotal: 3m 46s\tremaining: 59m 8s\n",
      "700:\tlearn: 0.8900556\ttest: 0.8849600\tbest: 0.8849800 (699)\ttotal: 4m 24s\tremaining: 58m 29s\n",
      "800:\tlearn: 0.8908489\ttest: 0.8853200\tbest: 0.8853800 (794)\ttotal: 5m 2s\tremaining: 57m 51s\n",
      "900:\tlearn: 0.8916711\ttest: 0.8856400\tbest: 0.8856800 (892)\ttotal: 5m 40s\tremaining: 57m 15s\n",
      "1000:\tlearn: 0.8924467\ttest: 0.8861200\tbest: 0.8861400 (980)\ttotal: 6m 18s\tremaining: 56m 39s\n",
      "1100:\tlearn: 0.8933556\ttest: 0.8863200\tbest: 0.8863600 (1081)\ttotal: 6m 56s\tremaining: 56m 6s\n",
      "1200:\tlearn: 0.8942800\ttest: 0.8864800\tbest: 0.8865000 (1197)\ttotal: 7m 34s\tremaining: 55m 31s\n",
      "1300:\tlearn: 0.8952978\ttest: 0.8867000\tbest: 0.8867200 (1244)\ttotal: 8m 13s\tremaining: 54m 56s\n",
      "1400:\tlearn: 0.8963467\ttest: 0.8869000\tbest: 0.8870800 (1395)\ttotal: 8m 51s\tremaining: 54m 22s\n",
      "1500:\tlearn: 0.8974578\ttest: 0.8873000\tbest: 0.8873200 (1498)\ttotal: 9m 29s\tremaining: 53m 46s\n",
      "1600:\tlearn: 0.8986778\ttest: 0.8874200\tbest: 0.8875600 (1568)\ttotal: 10m 8s\tremaining: 53m 11s\n",
      "1700:\tlearn: 0.8997822\ttest: 0.8876400\tbest: 0.8876400 (1696)\ttotal: 10m 46s\tremaining: 52m 36s\n",
      "1800:\tlearn: 0.9008400\ttest: 0.8877200\tbest: 0.8877600 (1762)\ttotal: 11m 25s\tremaining: 52m\n",
      "1900:\tlearn: 0.9020778\ttest: 0.8879400\tbest: 0.8880400 (1870)\ttotal: 12m 3s\tremaining: 51m 24s\n",
      "2000:\tlearn: 0.9032356\ttest: 0.8879800\tbest: 0.8881400 (1923)\ttotal: 12m 42s\tremaining: 50m 48s\n",
      "2100:\tlearn: 0.9043489\ttest: 0.8882600\tbest: 0.8882600 (2097)\ttotal: 13m 21s\tremaining: 50m 13s\n",
      "2200:\tlearn: 0.9055111\ttest: 0.8884000\tbest: 0.8884600 (2174)\ttotal: 14m\tremaining: 49m 37s\n",
      "2300:\tlearn: 0.9066422\ttest: 0.8882000\tbest: 0.8884600 (2174)\ttotal: 14m 38s\tremaining: 49m\n",
      "2400:\tlearn: 0.9076089\ttest: 0.8884400\tbest: 0.8884600 (2174)\ttotal: 15m 17s\tremaining: 48m 23s\n",
      "2500:\tlearn: 0.9087022\ttest: 0.8886200\tbest: 0.8886200 (2500)\ttotal: 15m 56s\tremaining: 47m 46s\n",
      "2600:\tlearn: 0.9097356\ttest: 0.8886600\tbest: 0.8887400 (2534)\ttotal: 16m 34s\tremaining: 47m 9s\n",
      "2700:\tlearn: 0.9110178\ttest: 0.8888600\tbest: 0.8889000 (2696)\ttotal: 17m 13s\tremaining: 46m 32s\n",
      "2800:\tlearn: 0.9120956\ttest: 0.8887400\tbest: 0.8889200 (2757)\ttotal: 17m 52s\tremaining: 45m 55s\n",
      "2900:\tlearn: 0.9132533\ttest: 0.8891000\tbest: 0.8891000 (2895)\ttotal: 18m 30s\tremaining: 45m 18s\n",
      "3000:\tlearn: 0.9142178\ttest: 0.8891800\tbest: 0.8892600 (2991)\ttotal: 19m 9s\tremaining: 44m 40s\n",
      "3100:\tlearn: 0.9153978\ttest: 0.8891000\tbest: 0.8892600 (2991)\ttotal: 19m 48s\tremaining: 44m 3s\n",
      "3200:\tlearn: 0.9165644\ttest: 0.8892000\tbest: 0.8892800 (3143)\ttotal: 20m 27s\tremaining: 43m 26s\n",
      "3300:\tlearn: 0.9176756\ttest: 0.8891800\tbest: 0.8892800 (3143)\ttotal: 21m 5s\tremaining: 42m 49s\n",
      "3400:\tlearn: 0.9187222\ttest: 0.8892600\tbest: 0.8892800 (3143)\ttotal: 21m 44s\tremaining: 42m 11s\n",
      "3500:\tlearn: 0.9199422\ttest: 0.8891200\tbest: 0.8892800 (3143)\ttotal: 22m 23s\tremaining: 41m 33s\n",
      "3600:\tlearn: 0.9211733\ttest: 0.8889800\tbest: 0.8892800 (3143)\ttotal: 23m 2s\tremaining: 40m 56s\n",
      "3700:\tlearn: 0.9222689\ttest: 0.8889000\tbest: 0.8892800 (3143)\ttotal: 23m 41s\tremaining: 40m 18s\n",
      "3800:\tlearn: 0.9233333\ttest: 0.8889200\tbest: 0.8892800 (3143)\ttotal: 24m 20s\tremaining: 39m 41s\n",
      "3900:\tlearn: 0.9244289\ttest: 0.8889400\tbest: 0.8892800 (3143)\ttotal: 24m 58s\tremaining: 39m 3s\n",
      "4000:\tlearn: 0.9255178\ttest: 0.8889800\tbest: 0.8892800 (3143)\ttotal: 25m 37s\tremaining: 38m 25s\n",
      "4100:\tlearn: 0.9266200\ttest: 0.8889800\tbest: 0.8892800 (3143)\ttotal: 26m 17s\tremaining: 37m 48s\n",
      "bestTest = 0.88928\n",
      "bestIteration = 3143\n",
      "Shrink model to first 3144 iterations.\n",
      "第3个子模型 accuracy0.88928\n",
      "3\n",
      "训练\n",
      "0:\tlearn: 0.8650133\ttest: 0.8651800\tbest: 0.8651800 (0)\ttotal: 383ms\tremaining: 1h 3m 46s\n",
      "100:\tlearn: 0.8762089\ttest: 0.8763400\tbest: 0.8764000 (94)\ttotal: 38.1s\tremaining: 1h 2m 18s\n",
      "200:\tlearn: 0.8806911\ttest: 0.8805400\tbest: 0.8805800 (197)\ttotal: 1m 15s\tremaining: 1h 1m 44s\n",
      "300:\tlearn: 0.8841933\ttest: 0.8836200\tbest: 0.8836200 (300)\ttotal: 1m 53s\tremaining: 1h 1m 6s\n",
      "400:\tlearn: 0.8854800\ttest: 0.8846600\tbest: 0.8846800 (399)\ttotal: 2m 31s\tremaining: 1h 26s\n",
      "500:\tlearn: 0.8870044\ttest: 0.8860200\tbest: 0.8860200 (500)\ttotal: 3m 9s\tremaining: 59m 47s\n",
      "600:\tlearn: 0.8886600\ttest: 0.8873600\tbest: 0.8873600 (600)\ttotal: 3m 47s\tremaining: 59m 11s\n",
      "700:\tlearn: 0.8895689\ttest: 0.8880000\tbest: 0.8880000 (693)\ttotal: 4m 24s\tremaining: 58m 33s\n",
      "800:\tlearn: 0.8904222\ttest: 0.8885200\tbest: 0.8885200 (800)\ttotal: 5m 2s\tremaining: 57m 55s\n",
      "900:\tlearn: 0.8913067\ttest: 0.8886400\tbest: 0.8888200 (864)\ttotal: 5m 40s\tremaining: 57m 19s\n",
      "1000:\tlearn: 0.8921111\ttest: 0.8890400\tbest: 0.8891000 (996)\ttotal: 6m 18s\tremaining: 56m 43s\n",
      "1100:\tlearn: 0.8930711\ttest: 0.8894000\tbest: 0.8894000 (1099)\ttotal: 6m 57s\tremaining: 56m 11s\n",
      "1200:\tlearn: 0.8941444\ttest: 0.8894600\tbest: 0.8896200 (1157)\ttotal: 7m 35s\tremaining: 55m 36s\n",
      "1300:\tlearn: 0.8951467\ttest: 0.8896800\tbest: 0.8897200 (1294)\ttotal: 8m 13s\tremaining: 55m\n",
      "1400:\tlearn: 0.8963533\ttest: 0.8898000\tbest: 0.8899400 (1373)\ttotal: 8m 52s\tremaining: 54m 25s\n",
      "1500:\tlearn: 0.8974822\ttest: 0.8900000\tbest: 0.8900600 (1487)\ttotal: 9m 30s\tremaining: 53m 50s\n",
      "1600:\tlearn: 0.8986733\ttest: 0.8903200\tbest: 0.8903800 (1575)\ttotal: 10m 9s\tremaining: 53m 14s\n",
      "1700:\tlearn: 0.8997978\ttest: 0.8903800\tbest: 0.8904800 (1668)\ttotal: 10m 47s\tremaining: 52m 39s\n",
      "1800:\tlearn: 0.9008733\ttest: 0.8904400\tbest: 0.8905000 (1711)\ttotal: 11m 26s\tremaining: 52m 4s\n",
      "1900:\tlearn: 0.9020489\ttest: 0.8903200\tbest: 0.8905600 (1820)\ttotal: 12m 4s\tremaining: 51m 28s\n",
      "2000:\tlearn: 0.9031956\ttest: 0.8903400\tbest: 0.8905600 (1820)\ttotal: 12m 43s\tremaining: 50m 52s\n",
      "2100:\tlearn: 0.9043378\ttest: 0.8904000\tbest: 0.8905600 (1820)\ttotal: 13m 22s\tremaining: 50m 17s\n",
      "2200:\tlearn: 0.9054133\ttest: 0.8905600\tbest: 0.8906200 (2157)\ttotal: 14m 1s\tremaining: 49m 41s\n",
      "2300:\tlearn: 0.9067533\ttest: 0.8907000\tbest: 0.8907400 (2296)\ttotal: 14m 40s\tremaining: 49m 4s\n",
      "2400:\tlearn: 0.9079733\ttest: 0.8905800\tbest: 0.8907600 (2329)\ttotal: 15m 18s\tremaining: 48m 27s\n",
      "2500:\tlearn: 0.9090356\ttest: 0.8905200\tbest: 0.8907600 (2329)\ttotal: 15m 57s\tremaining: 47m 50s\n",
      "2600:\tlearn: 0.9101778\ttest: 0.8907400\tbest: 0.8908600 (2584)\ttotal: 16m 36s\tremaining: 47m 13s\n",
      "2700:\tlearn: 0.9113089\ttest: 0.8908400\tbest: 0.8909800 (2665)\ttotal: 17m 15s\tremaining: 46m 37s\n",
      "2800:\tlearn: 0.9124222\ttest: 0.8908400\tbest: 0.8909800 (2665)\ttotal: 17m 53s\tremaining: 46m\n",
      "2900:\tlearn: 0.9135800\ttest: 0.8908200\tbest: 0.8909800 (2665)\ttotal: 18m 32s\tremaining: 45m 22s\n",
      "3000:\tlearn: 0.9146867\ttest: 0.8906800\tbest: 0.8909800 (2665)\ttotal: 19m 11s\tremaining: 44m 45s\n",
      "3100:\tlearn: 0.9159800\ttest: 0.8906000\tbest: 0.8909800 (2665)\ttotal: 19m 50s\tremaining: 44m 7s\n",
      "3200:\tlearn: 0.9170711\ttest: 0.8905200\tbest: 0.8909800 (2665)\ttotal: 20m 28s\tremaining: 43m 30s\n",
      "3300:\tlearn: 0.9183089\ttest: 0.8905200\tbest: 0.8909800 (2665)\ttotal: 21m 7s\tremaining: 42m 52s\n",
      "3400:\tlearn: 0.9194644\ttest: 0.8904000\tbest: 0.8909800 (2665)\ttotal: 21m 46s\tremaining: 42m 15s\n",
      "3500:\tlearn: 0.9205556\ttest: 0.8903800\tbest: 0.8909800 (2665)\ttotal: 22m 25s\tremaining: 41m 37s\n",
      "3600:\tlearn: 0.9216800\ttest: 0.8902600\tbest: 0.8909800 (2665)\ttotal: 23m 4s\tremaining: 41m\n",
      "bestTest = 0.89098\n",
      "bestIteration = 2665\n",
      "Shrink model to first 2666 iterations.\n",
      "第4个子模型 accuracy0.89098\n",
      "4\n",
      "训练\n",
      "0:\tlearn: 0.8623289\ttest: 0.8622200\tbest: 0.8622200 (0)\ttotal: 396ms\tremaining: 1h 6m 4s\n",
      "100:\tlearn: 0.8764867\ttest: 0.8762000\tbest: 0.8762000 (100)\ttotal: 38.2s\tremaining: 1h 2m 19s\n",
      "200:\tlearn: 0.8813356\ttest: 0.8808800\tbest: 0.8809400 (198)\ttotal: 1m 15s\tremaining: 1h 1m 40s\n",
      "300:\tlearn: 0.8839600\ttest: 0.8833400\tbest: 0.8833600 (294)\ttotal: 1m 53s\tremaining: 1h 1m 4s\n",
      "400:\tlearn: 0.8855556\ttest: 0.8844800\tbest: 0.8844800 (399)\ttotal: 2m 31s\tremaining: 1h 25s\n",
      "500:\tlearn: 0.8871267\ttest: 0.8857000\tbest: 0.8857000 (498)\ttotal: 3m 9s\tremaining: 59m 48s\n",
      "600:\tlearn: 0.8887556\ttest: 0.8868400\tbest: 0.8869000 (592)\ttotal: 3m 47s\tremaining: 59m 13s\n",
      "700:\tlearn: 0.8898267\ttest: 0.8873600\tbest: 0.8874200 (687)\ttotal: 4m 24s\tremaining: 58m 35s\n",
      "800:\tlearn: 0.8906711\ttest: 0.8880600\tbest: 0.8881000 (797)\ttotal: 5m 2s\tremaining: 57m 57s\n",
      "900:\tlearn: 0.8915089\ttest: 0.8882600\tbest: 0.8883000 (886)\ttotal: 5m 40s\tremaining: 57m 22s\n",
      "1000:\tlearn: 0.8923667\ttest: 0.8884000\tbest: 0.8884800 (979)\ttotal: 6m 18s\tremaining: 56m 46s\n",
      "1100:\tlearn: 0.8932822\ttest: 0.8885600\tbest: 0.8885800 (1090)\ttotal: 6m 57s\tremaining: 56m 14s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1200:\tlearn: 0.8942644\ttest: 0.8888600\tbest: 0.8888600 (1200)\ttotal: 7m 35s\tremaining: 55m 39s\n",
      "1300:\tlearn: 0.8952311\ttest: 0.8891400\tbest: 0.8891400 (1295)\ttotal: 8m 14s\tremaining: 55m 3s\n",
      "1400:\tlearn: 0.8963200\ttest: 0.8892600\tbest: 0.8893000 (1369)\ttotal: 8m 52s\tremaining: 54m 28s\n",
      "1500:\tlearn: 0.8976356\ttest: 0.8895600\tbest: 0.8897000 (1487)\ttotal: 9m 31s\tremaining: 53m 53s\n",
      "1600:\tlearn: 0.8987578\ttest: 0.8896800\tbest: 0.8897000 (1487)\ttotal: 10m 9s\tremaining: 53m 17s\n",
      "1700:\tlearn: 0.8998533\ttest: 0.8897000\tbest: 0.8898400 (1674)\ttotal: 10m 48s\tremaining: 52m 42s\n",
      "1800:\tlearn: 0.9010556\ttest: 0.8897000\tbest: 0.8898400 (1674)\ttotal: 11m 26s\tremaining: 52m 6s\n",
      "1900:\tlearn: 0.9022222\ttest: 0.8899000\tbest: 0.8899800 (1882)\ttotal: 12m 5s\tremaining: 51m 31s\n",
      "2000:\tlearn: 0.9034356\ttest: 0.8901000\tbest: 0.8901600 (1997)\ttotal: 12m 44s\tremaining: 50m 55s\n",
      "2100:\tlearn: 0.9045756\ttest: 0.8901400\tbest: 0.8902000 (2009)\ttotal: 13m 23s\tremaining: 50m 21s\n",
      "2200:\tlearn: 0.9057956\ttest: 0.8903400\tbest: 0.8903400 (2200)\ttotal: 14m 2s\tremaining: 49m 45s\n",
      "2300:\tlearn: 0.9068133\ttest: 0.8904000\tbest: 0.8904200 (2212)\ttotal: 14m 41s\tremaining: 49m 8s\n",
      "2400:\tlearn: 0.9079667\ttest: 0.8905400\tbest: 0.8906200 (2394)\ttotal: 15m 19s\tremaining: 48m 31s\n",
      "2500:\tlearn: 0.9089533\ttest: 0.8905000\tbest: 0.8906200 (2394)\ttotal: 15m 58s\tremaining: 47m 54s\n",
      "2600:\tlearn: 0.9101556\ttest: 0.8907200\tbest: 0.8907600 (2596)\ttotal: 16m 37s\tremaining: 47m 17s\n",
      "2700:\tlearn: 0.9113822\ttest: 0.8906400\tbest: 0.8907600 (2596)\ttotal: 17m 16s\tremaining: 46m 40s\n",
      "2800:\tlearn: 0.9126667\ttest: 0.8906400\tbest: 0.8908200 (2726)\ttotal: 17m 55s\tremaining: 46m 3s\n",
      "2900:\tlearn: 0.9138111\ttest: 0.8907200\tbest: 0.8908200 (2726)\ttotal: 18m 33s\tremaining: 45m 25s\n",
      "3000:\tlearn: 0.9150556\ttest: 0.8908200\tbest: 0.8908400 (2999)\ttotal: 19m 12s\tremaining: 44m 48s\n",
      "3100:\tlearn: 0.9162867\ttest: 0.8909200\tbest: 0.8910200 (3035)\ttotal: 19m 51s\tremaining: 44m 11s\n",
      "3200:\tlearn: 0.9173200\ttest: 0.8909600\tbest: 0.8910400 (3172)\ttotal: 20m 30s\tremaining: 43m 33s\n",
      "3300:\tlearn: 0.9184089\ttest: 0.8907600\tbest: 0.8910400 (3172)\ttotal: 21m 9s\tremaining: 42m 56s\n",
      "3400:\tlearn: 0.9195044\ttest: 0.8907800\tbest: 0.8910400 (3172)\ttotal: 21m 48s\tremaining: 42m 18s\n",
      "3500:\tlearn: 0.9206378\ttest: 0.8907400\tbest: 0.8910400 (3172)\ttotal: 22m 27s\tremaining: 41m 41s\n",
      "3600:\tlearn: 0.9218511\ttest: 0.8906600\tbest: 0.8910400 (3172)\ttotal: 23m 6s\tremaining: 41m 3s\n",
      "3700:\tlearn: 0.9229889\ttest: 0.8907400\tbest: 0.8910400 (3172)\ttotal: 23m 45s\tremaining: 40m 26s\n",
      "3800:\tlearn: 0.9241267\ttest: 0.8908800\tbest: 0.8910400 (3172)\ttotal: 24m 24s\tremaining: 39m 48s\n",
      "3900:\tlearn: 0.9253111\ttest: 0.8908200\tbest: 0.8910400 (3172)\ttotal: 25m 3s\tremaining: 39m 10s\n",
      "4000:\tlearn: 0.9263244\ttest: 0.8908800\tbest: 0.8910400 (3172)\ttotal: 25m 42s\tremaining: 38m 32s\n",
      "4100:\tlearn: 0.9273400\ttest: 0.8909000\tbest: 0.8910800 (4069)\ttotal: 26m 22s\tremaining: 37m 56s\n",
      "4200:\tlearn: 0.9283844\ttest: 0.8908600\tbest: 0.8910800 (4069)\ttotal: 27m 1s\tremaining: 37m 17s\n",
      "4300:\tlearn: 0.9295378\ttest: 0.8910000\tbest: 0.8910800 (4069)\ttotal: 27m 40s\tremaining: 36m 39s\n",
      "4400:\tlearn: 0.9306844\ttest: 0.8909200\tbest: 0.8910800 (4069)\ttotal: 28m 19s\tremaining: 36m 1s\n",
      "4500:\tlearn: 0.9315711\ttest: 0.8907600\tbest: 0.8910800 (4069)\ttotal: 28m 57s\tremaining: 35m 23s\n",
      "4600:\tlearn: 0.9324533\ttest: 0.8907400\tbest: 0.8910800 (4069)\ttotal: 29m 36s\tremaining: 34m 44s\n",
      "4700:\tlearn: 0.9334133\ttest: 0.8907400\tbest: 0.8910800 (4069)\ttotal: 30m 15s\tremaining: 34m 6s\n",
      "4800:\tlearn: 0.9343822\ttest: 0.8908200\tbest: 0.8910800 (4069)\ttotal: 30m 54s\tremaining: 33m 28s\n",
      "4900:\tlearn: 0.9352844\ttest: 0.8908000\tbest: 0.8910800 (4069)\ttotal: 31m 33s\tremaining: 32m 50s\n",
      "5000:\tlearn: 0.9363467\ttest: 0.8908200\tbest: 0.8910800 (4069)\ttotal: 32m 12s\tremaining: 32m 11s\n",
      "bestTest = 0.89108\n",
      "bestIteration = 4069\n",
      "Shrink model to first 4070 iterations.\n",
      "第5个子模型 accuracy0.89108\n",
      "5\n",
      "训练\n",
      "0:\tlearn: 0.8646156\ttest: 0.8643200\tbest: 0.8643200 (0)\ttotal: 397ms\tremaining: 1h 6m 5s\n",
      "100:\tlearn: 0.8758489\ttest: 0.8776600\tbest: 0.8777400 (97)\ttotal: 38.2s\tremaining: 1h 2m 21s\n",
      "200:\tlearn: 0.8801911\ttest: 0.8812600\tbest: 0.8813600 (192)\ttotal: 1m 15s\tremaining: 1h 1m 43s\n",
      "300:\tlearn: 0.8838333\ttest: 0.8843200\tbest: 0.8843200 (297)\ttotal: 1m 53s\tremaining: 1h 1m 6s\n",
      "400:\tlearn: 0.8851911\ttest: 0.8858800\tbest: 0.8858800 (400)\ttotal: 2m 31s\tremaining: 1h 26s\n",
      "500:\tlearn: 0.8871133\ttest: 0.8875600\tbest: 0.8875600 (499)\ttotal: 3m 9s\tremaining: 59m 48s\n",
      "600:\tlearn: 0.8888667\ttest: 0.8884600\tbest: 0.8884600 (599)\ttotal: 3m 47s\tremaining: 59m 13s\n",
      "700:\tlearn: 0.8897400\ttest: 0.8891400\tbest: 0.8891400 (699)\ttotal: 4m 24s\tremaining: 58m 34s\n",
      "800:\tlearn: 0.8908044\ttest: 0.8887800\tbest: 0.8891800 (713)\ttotal: 5m 2s\tremaining: 57m 56s\n",
      "900:\tlearn: 0.8915489\ttest: 0.8893600\tbest: 0.8894000 (886)\ttotal: 5m 40s\tremaining: 57m 20s\n",
      "1000:\tlearn: 0.8922889\ttest: 0.8894200\tbest: 0.8894600 (922)\ttotal: 6m 18s\tremaining: 56m 45s\n",
      "1100:\tlearn: 0.8932422\ttest: 0.8895000\tbest: 0.8895800 (1082)\ttotal: 6m 57s\tremaining: 56m 12s\n",
      "1200:\tlearn: 0.8941778\ttest: 0.8897400\tbest: 0.8898400 (1175)\ttotal: 7m 35s\tremaining: 55m 37s\n",
      "1300:\tlearn: 0.8950867\ttest: 0.8898600\tbest: 0.8900000 (1248)\ttotal: 8m 13s\tremaining: 55m 2s\n",
      "1400:\tlearn: 0.8962000\ttest: 0.8898600\tbest: 0.8900000 (1248)\ttotal: 8m 52s\tremaining: 54m 26s\n",
      "1500:\tlearn: 0.8973778\ttest: 0.8896600\tbest: 0.8900000 (1248)\ttotal: 9m 30s\tremaining: 53m 51s\n",
      "1600:\tlearn: 0.8983689\ttest: 0.8900000\tbest: 0.8902200 (1577)\ttotal: 10m 9s\tremaining: 53m 15s\n",
      "1700:\tlearn: 0.8994644\ttest: 0.8902600\tbest: 0.8903000 (1677)\ttotal: 10m 47s\tremaining: 52m 40s\n",
      "1800:\tlearn: 0.9007556\ttest: 0.8903000\tbest: 0.8905000 (1793)\ttotal: 11m 26s\tremaining: 52m 5s\n",
      "1900:\tlearn: 0.9019800\ttest: 0.8904000\tbest: 0.8905200 (1831)\ttotal: 12m 5s\tremaining: 51m 30s\n",
      "2000:\tlearn: 0.9029889\ttest: 0.8908000\tbest: 0.8908000 (1997)\ttotal: 12m 44s\tremaining: 50m 54s\n",
      "2100:\tlearn: 0.9040578\ttest: 0.8908200\tbest: 0.8908600 (2002)\ttotal: 13m 23s\tremaining: 50m 21s\n",
      "2200:\tlearn: 0.9051733\ttest: 0.8907200\tbest: 0.8908600 (2002)\ttotal: 14m 2s\tremaining: 49m 44s\n",
      "2300:\tlearn: 0.9064267\ttest: 0.8906200\tbest: 0.8909000 (2249)\ttotal: 14m 41s\tremaining: 49m 8s\n",
      "2400:\tlearn: 0.9075089\ttest: 0.8907200\tbest: 0.8909000 (2249)\ttotal: 15m 19s\tremaining: 48m 31s\n",
      "2500:\tlearn: 0.9087000\ttest: 0.8908200\tbest: 0.8909000 (2249)\ttotal: 15m 58s\tremaining: 47m 54s\n",
      "2600:\tlearn: 0.9097178\ttest: 0.8907400\tbest: 0.8909000 (2249)\ttotal: 16m 37s\tremaining: 47m 17s\n",
      "2700:\tlearn: 0.9108689\ttest: 0.8909800\tbest: 0.8910400 (2695)\ttotal: 17m 16s\tremaining: 46m 40s\n",
      "2800:\tlearn: 0.9121156\ttest: 0.8907000\tbest: 0.8910400 (2695)\ttotal: 17m 55s\tremaining: 46m 3s\n",
      "2900:\tlearn: 0.9133000\ttest: 0.8907600\tbest: 0.8910400 (2695)\ttotal: 18m 34s\tremaining: 45m 27s\n",
      "3000:\tlearn: 0.9146111\ttest: 0.8905200\tbest: 0.8910400 (2695)\ttotal: 19m 13s\tremaining: 44m 50s\n",
      "3100:\tlearn: 0.9158822\ttest: 0.8905400\tbest: 0.8910400 (2695)\ttotal: 19m 52s\tremaining: 44m 13s\n",
      "3200:\tlearn: 0.9171311\ttest: 0.8907400\tbest: 0.8910400 (2695)\ttotal: 20m 31s\tremaining: 43m 36s\n",
      "3300:\tlearn: 0.9183667\ttest: 0.8906400\tbest: 0.8910400 (2695)\ttotal: 21m 11s\tremaining: 42m 59s\n",
      "3400:\tlearn: 0.9195444\ttest: 0.8909000\tbest: 0.8910400 (2695)\ttotal: 21m 50s\tremaining: 42m 22s\n",
      "3500:\tlearn: 0.9206889\ttest: 0.8908600\tbest: 0.8910400 (2695)\ttotal: 22m 29s\tremaining: 41m 44s\n",
      "3600:\tlearn: 0.9217489\ttest: 0.8908000\tbest: 0.8910400 (2695)\ttotal: 23m 8s\tremaining: 41m 7s\n",
      "bestTest = 0.89104\n",
      "bestIteration = 2695\n",
      "Shrink model to first 2696 iterations.\n",
      "第6个子模型 accuracy0.89104\n",
      "6\n",
      "训练\n",
      "0:\tlearn: 0.8602556\ttest: 0.8637000\tbest: 0.8637000 (0)\ttotal: 384ms\tremaining: 1h 3m 59s\n",
      "100:\tlearn: 0.8768200\ttest: 0.8803600\tbest: 0.8805200 (99)\ttotal: 38.1s\tremaining: 1h 2m 16s\n",
      "200:\tlearn: 0.8807956\ttest: 0.8831600\tbest: 0.8831600 (200)\ttotal: 1m 15s\tremaining: 1h 1m 37s\n",
      "300:\tlearn: 0.8836178\ttest: 0.8856600\tbest: 0.8856600 (300)\ttotal: 1m 53s\tremaining: 1h 1m 2s\n",
      "400:\tlearn: 0.8853178\ttest: 0.8869600\tbest: 0.8870200 (399)\ttotal: 2m 31s\tremaining: 1h 23s\n",
      "500:\tlearn: 0.8870222\ttest: 0.8885400\tbest: 0.8885400 (500)\ttotal: 3m 9s\tremaining: 59m 45s\n",
      "600:\tlearn: 0.8884978\ttest: 0.8895600\tbest: 0.8895600 (599)\ttotal: 3m 46s\tremaining: 59m 9s\n",
      "700:\tlearn: 0.8894844\ttest: 0.8900200\tbest: 0.8900200 (692)\ttotal: 4m 24s\tremaining: 58m 30s\n",
      "800:\tlearn: 0.8903178\ttest: 0.8905800\tbest: 0.8906400 (795)\ttotal: 5m 2s\tremaining: 57m 52s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "900:\tlearn: 0.8910600\ttest: 0.8907800\tbest: 0.8908800 (889)\ttotal: 5m 40s\tremaining: 57m 16s\n",
      "1000:\tlearn: 0.8918156\ttest: 0.8913800\tbest: 0.8913800 (1000)\ttotal: 6m 18s\tremaining: 56m 41s\n",
      "1100:\tlearn: 0.8927800\ttest: 0.8917000\tbest: 0.8917000 (1100)\ttotal: 6m 56s\tremaining: 56m 8s\n",
      "1200:\tlearn: 0.8937889\ttest: 0.8917800\tbest: 0.8918200 (1188)\ttotal: 7m 35s\tremaining: 55m 34s\n",
      "1300:\tlearn: 0.8950289\ttest: 0.8921400\tbest: 0.8921400 (1300)\ttotal: 8m 13s\tremaining: 55m\n",
      "1400:\tlearn: 0.8962889\ttest: 0.8922800\tbest: 0.8923200 (1365)\ttotal: 8m 52s\tremaining: 54m 25s\n",
      "1500:\tlearn: 0.8975644\ttest: 0.8923800\tbest: 0.8925200 (1497)\ttotal: 9m 30s\tremaining: 53m 51s\n",
      "1600:\tlearn: 0.8988289\ttest: 0.8927600\tbest: 0.8927800 (1599)\ttotal: 10m 9s\tremaining: 53m 16s\n",
      "1700:\tlearn: 0.8999467\ttest: 0.8928600\tbest: 0.8929600 (1696)\ttotal: 10m 48s\tremaining: 52m 41s\n",
      "1800:\tlearn: 0.9010644\ttest: 0.8929600\tbest: 0.8931000 (1790)\ttotal: 11m 26s\tremaining: 52m 6s\n",
      "1900:\tlearn: 0.9021311\ttest: 0.8929800\tbest: 0.8931600 (1862)\ttotal: 12m 5s\tremaining: 51m 31s\n",
      "2000:\tlearn: 0.9032800\ttest: 0.8930000\tbest: 0.8931600 (1862)\ttotal: 12m 44s\tremaining: 50m 55s\n",
      "2100:\tlearn: 0.9044044\ttest: 0.8931600\tbest: 0.8931800 (2035)\ttotal: 13m 23s\tremaining: 50m 22s\n",
      "2200:\tlearn: 0.9055200\ttest: 0.8932000\tbest: 0.8932200 (2113)\ttotal: 14m 2s\tremaining: 49m 45s\n",
      "2300:\tlearn: 0.9067289\ttest: 0.8932400\tbest: 0.8933400 (2245)\ttotal: 14m 41s\tremaining: 49m 9s\n",
      "2400:\tlearn: 0.9078467\ttest: 0.8934600\tbest: 0.8934600 (2397)\ttotal: 15m 20s\tremaining: 48m 32s\n",
      "2500:\tlearn: 0.9090089\ttest: 0.8934000\tbest: 0.8935000 (2477)\ttotal: 15m 59s\tremaining: 47m 56s\n",
      "2600:\tlearn: 0.9101111\ttest: 0.8934000\tbest: 0.8935000 (2477)\ttotal: 16m 38s\tremaining: 47m 19s\n",
      "2700:\tlearn: 0.9113200\ttest: 0.8936400\tbest: 0.8937400 (2694)\ttotal: 17m 17s\tremaining: 46m 42s\n",
      "2800:\tlearn: 0.9126000\ttest: 0.8936000\tbest: 0.8937400 (2694)\ttotal: 17m 55s\tremaining: 46m 5s\n",
      "2900:\tlearn: 0.9137222\ttest: 0.8936200\tbest: 0.8937400 (2694)\ttotal: 18m 34s\tremaining: 45m 28s\n",
      "3000:\tlearn: 0.9148889\ttest: 0.8937800\tbest: 0.8938000 (2955)\ttotal: 19m 13s\tremaining: 44m 51s\n",
      "3100:\tlearn: 0.9161822\ttest: 0.8939000\tbest: 0.8939400 (3051)\ttotal: 19m 53s\tremaining: 44m 14s\n",
      "3200:\tlearn: 0.9174356\ttest: 0.8937800\tbest: 0.8939800 (3115)\ttotal: 20m 32s\tremaining: 43m 37s\n",
      "3300:\tlearn: 0.9186911\ttest: 0.8936200\tbest: 0.8939800 (3115)\ttotal: 21m 11s\tremaining: 42m 59s\n",
      "3400:\tlearn: 0.9197133\ttest: 0.8937000\tbest: 0.8939800 (3115)\ttotal: 21m 50s\tremaining: 42m 22s\n",
      "3500:\tlearn: 0.9210378\ttest: 0.8934600\tbest: 0.8939800 (3115)\ttotal: 22m 29s\tremaining: 41m 44s\n",
      "3600:\tlearn: 0.9222578\ttest: 0.8935600\tbest: 0.8939800 (3115)\ttotal: 23m 8s\tremaining: 41m 7s\n",
      "3700:\tlearn: 0.9234556\ttest: 0.8934800\tbest: 0.8939800 (3115)\ttotal: 23m 47s\tremaining: 40m 30s\n",
      "3800:\tlearn: 0.9244933\ttest: 0.8935800\tbest: 0.8939800 (3115)\ttotal: 24m 27s\tremaining: 39m 52s\n",
      "3900:\tlearn: 0.9254822\ttest: 0.8933600\tbest: 0.8939800 (3115)\ttotal: 25m 6s\tremaining: 39m 14s\n",
      "4000:\tlearn: 0.9265578\ttest: 0.8932400\tbest: 0.8939800 (3115)\ttotal: 25m 45s\tremaining: 38m 36s\n",
      "4100:\tlearn: 0.9276667\ttest: 0.8931200\tbest: 0.8939800 (3115)\ttotal: 26m 25s\tremaining: 38m\n",
      "bestTest = 0.89398\n",
      "bestIteration = 3115\n",
      "Shrink model to first 3116 iterations.\n",
      "第7个子模型 accuracy0.89398\n",
      "7\n",
      "训练\n",
      "0:\tlearn: 0.8653511\ttest: 0.8621200\tbest: 0.8621200 (0)\ttotal: 389ms\tremaining: 1h 4m 47s\n",
      "100:\tlearn: 0.8768356\ttest: 0.8734600\tbest: 0.8734800 (93)\ttotal: 38.1s\tremaining: 1h 2m 18s\n",
      "200:\tlearn: 0.8814333\ttest: 0.8788200\tbest: 0.8789200 (199)\ttotal: 1m 15s\tremaining: 1h 1m 40s\n",
      "300:\tlearn: 0.8841867\ttest: 0.8814000\tbest: 0.8814000 (300)\ttotal: 1m 53s\tremaining: 1h 1m 2s\n",
      "400:\tlearn: 0.8856578\ttest: 0.8823800\tbest: 0.8824000 (395)\ttotal: 2m 31s\tremaining: 1h 24s\n",
      "500:\tlearn: 0.8871244\ttest: 0.8840200\tbest: 0.8840200 (500)\ttotal: 3m 9s\tremaining: 59m 46s\n",
      "600:\tlearn: 0.8887089\ttest: 0.8850400\tbest: 0.8850600 (599)\ttotal: 3m 47s\tremaining: 59m 10s\n",
      "700:\tlearn: 0.8897000\ttest: 0.8859200\tbest: 0.8859400 (697)\ttotal: 4m 24s\tremaining: 58m 32s\n",
      "800:\tlearn: 0.8906667\ttest: 0.8861400\tbest: 0.8861400 (786)\ttotal: 5m 2s\tremaining: 57m 55s\n",
      "900:\tlearn: 0.8915778\ttest: 0.8866400\tbest: 0.8866800 (892)\ttotal: 5m 40s\tremaining: 57m 19s\n",
      "1000:\tlearn: 0.8923867\ttest: 0.8869800\tbest: 0.8870400 (990)\ttotal: 6m 18s\tremaining: 56m 44s\n",
      "1100:\tlearn: 0.8931911\ttest: 0.8873400\tbest: 0.8873400 (1100)\ttotal: 6m 57s\tremaining: 56m 10s\n",
      "1200:\tlearn: 0.8941400\ttest: 0.8875000\tbest: 0.8875800 (1197)\ttotal: 7m 35s\tremaining: 55m 35s\n",
      "1300:\tlearn: 0.8951756\ttest: 0.8876200\tbest: 0.8876400 (1299)\ttotal: 8m 13s\tremaining: 55m\n",
      "1400:\tlearn: 0.8962133\ttest: 0.8874400\tbest: 0.8876400 (1299)\ttotal: 8m 52s\tremaining: 54m 25s\n",
      "1500:\tlearn: 0.8973822\ttest: 0.8874400\tbest: 0.8876400 (1299)\ttotal: 9m 30s\tremaining: 53m 50s\n",
      "1600:\tlearn: 0.8985556\ttest: 0.8878200\tbest: 0.8878200 (1598)\ttotal: 10m 9s\tremaining: 53m 15s\n",
      "1700:\tlearn: 0.8997311\ttest: 0.8878600\tbest: 0.8879400 (1637)\ttotal: 10m 47s\tremaining: 52m 40s\n",
      "1800:\tlearn: 0.9010311\ttest: 0.8883000\tbest: 0.8883400 (1796)\ttotal: 11m 26s\tremaining: 52m 5s\n",
      "1900:\tlearn: 0.9020333\ttest: 0.8883200\tbest: 0.8884400 (1886)\ttotal: 12m 5s\tremaining: 51m 29s\n",
      "2000:\tlearn: 0.9032289\ttest: 0.8884200\tbest: 0.8885200 (1965)\ttotal: 12m 44s\tremaining: 50m 54s\n",
      "2100:\tlearn: 0.9043000\ttest: 0.8884400\tbest: 0.8886000 (2049)\ttotal: 13m 23s\tremaining: 50m 20s\n",
      "2200:\tlearn: 0.9054956\ttest: 0.8884800\tbest: 0.8886000 (2049)\ttotal: 14m 2s\tremaining: 49m 44s\n",
      "2300:\tlearn: 0.9066533\ttest: 0.8885400\tbest: 0.8886000 (2049)\ttotal: 14m 41s\tremaining: 49m 8s\n",
      "2400:\tlearn: 0.9077378\ttest: 0.8885800\tbest: 0.8886000 (2049)\ttotal: 15m 20s\tremaining: 48m 31s\n",
      "2500:\tlearn: 0.9087178\ttest: 0.8888000\tbest: 0.8888000 (2498)\ttotal: 15m 58s\tremaining: 47m 54s\n",
      "2600:\tlearn: 0.9100289\ttest: 0.8887000\tbest: 0.8888600 (2505)\ttotal: 16m 37s\tremaining: 47m 18s\n",
      "2700:\tlearn: 0.9111044\ttest: 0.8888000\tbest: 0.8888600 (2505)\ttotal: 17m 16s\tremaining: 46m 41s\n",
      "2800:\tlearn: 0.9122267\ttest: 0.8888000\tbest: 0.8889200 (2783)\ttotal: 17m 55s\tremaining: 46m 4s\n",
      "2900:\tlearn: 0.9133800\ttest: 0.8887000\tbest: 0.8889200 (2783)\ttotal: 18m 34s\tremaining: 45m 27s\n",
      "3000:\tlearn: 0.9145911\ttest: 0.8887200\tbest: 0.8889200 (2783)\ttotal: 19m 13s\tremaining: 44m 50s\n",
      "3100:\tlearn: 0.9157667\ttest: 0.8888400\tbest: 0.8889200 (2783)\ttotal: 19m 52s\tremaining: 44m 13s\n",
      "3200:\tlearn: 0.9169956\ttest: 0.8889400\tbest: 0.8890000 (3152)\ttotal: 20m 31s\tremaining: 43m 36s\n",
      "3300:\tlearn: 0.9182800\ttest: 0.8888000\tbest: 0.8890000 (3152)\ttotal: 21m 10s\tremaining: 42m 59s\n",
      "3400:\tlearn: 0.9193578\ttest: 0.8888200\tbest: 0.8890000 (3152)\ttotal: 21m 50s\tremaining: 42m 21s\n",
      "3500:\tlearn: 0.9205289\ttest: 0.8886400\tbest: 0.8890000 (3152)\ttotal: 22m 29s\tremaining: 41m 44s\n",
      "3600:\tlearn: 0.9217489\ttest: 0.8887600\tbest: 0.8890000 (3152)\ttotal: 23m 8s\tremaining: 41m 7s\n",
      "3700:\tlearn: 0.9227422\ttest: 0.8890200\tbest: 0.8890200 (3696)\ttotal: 23m 47s\tremaining: 40m 29s\n",
      "3800:\tlearn: 0.9239844\ttest: 0.8891200\tbest: 0.8892400 (3793)\ttotal: 24m 26s\tremaining: 39m 52s\n",
      "3900:\tlearn: 0.9249800\ttest: 0.8891600\tbest: 0.8892400 (3793)\ttotal: 25m 6s\tremaining: 39m 14s\n",
      "4000:\tlearn: 0.9261067\ttest: 0.8891200\tbest: 0.8892400 (3793)\ttotal: 25m 45s\tremaining: 38m 37s\n",
      "4100:\tlearn: 0.9272378\ttest: 0.8888600\tbest: 0.8892400 (3793)\ttotal: 26m 25s\tremaining: 38m 1s\n",
      "4200:\tlearn: 0.9283178\ttest: 0.8888400\tbest: 0.8892400 (3793)\ttotal: 27m 4s\tremaining: 37m 22s\n",
      "4300:\tlearn: 0.9293267\ttest: 0.8889600\tbest: 0.8892400 (3793)\ttotal: 27m 43s\tremaining: 36m 44s\n",
      "4400:\tlearn: 0.9303889\ttest: 0.8888600\tbest: 0.8892400 (3793)\ttotal: 28m 22s\tremaining: 36m 6s\n",
      "4500:\tlearn: 0.9315600\ttest: 0.8888000\tbest: 0.8892400 (3793)\ttotal: 29m 2s\tremaining: 35m 28s\n",
      "4600:\tlearn: 0.9325156\ttest: 0.8889000\tbest: 0.8892400 (3793)\ttotal: 29m 41s\tremaining: 34m 50s\n",
      "4700:\tlearn: 0.9334667\ttest: 0.8889000\tbest: 0.8892400 (3793)\ttotal: 30m 20s\tremaining: 34m 11s\n",
      "bestTest = 0.88924\n",
      "bestIteration = 3793\n",
      "Shrink model to first 3794 iterations.\n",
      "第8个子模型 accuracy0.88924\n",
      "8\n",
      "训练\n",
      "0:\tlearn: 0.8606778\ttest: 0.8602200\tbest: 0.8602200 (0)\ttotal: 382ms\tremaining: 1h 3m 40s\n",
      "100:\tlearn: 0.8767822\ttest: 0.8755600\tbest: 0.8755600 (100)\ttotal: 38.2s\tremaining: 1h 2m 23s\n",
      "200:\tlearn: 0.8816200\ttest: 0.8799000\tbest: 0.8800000 (198)\ttotal: 1m 15s\tremaining: 1h 1m 43s\n",
      "300:\tlearn: 0.8838222\ttest: 0.8819400\tbest: 0.8819400 (300)\ttotal: 1m 53s\tremaining: 1h 1m 8s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "400:\tlearn: 0.8855444\ttest: 0.8838000\tbest: 0.8838000 (399)\ttotal: 2m 31s\tremaining: 1h 29s\n",
      "500:\tlearn: 0.8869467\ttest: 0.8853000\tbest: 0.8853000 (500)\ttotal: 3m 9s\tremaining: 59m 52s\n",
      "600:\tlearn: 0.8886822\ttest: 0.8868000\tbest: 0.8868000 (599)\ttotal: 3m 47s\tremaining: 59m 16s\n",
      "700:\tlearn: 0.8899067\ttest: 0.8872400\tbest: 0.8873200 (693)\ttotal: 4m 25s\tremaining: 58m 37s\n",
      "800:\tlearn: 0.8906178\ttest: 0.8874600\tbest: 0.8876600 (779)\ttotal: 5m 3s\tremaining: 58m\n",
      "900:\tlearn: 0.8914489\ttest: 0.8877800\tbest: 0.8878200 (898)\ttotal: 5m 41s\tremaining: 57m 24s\n",
      "1000:\tlearn: 0.8923822\ttest: 0.8881200\tbest: 0.8881600 (999)\ttotal: 6m 19s\tremaining: 56m 49s\n",
      "1100:\tlearn: 0.8932511\ttest: 0.8881400\tbest: 0.8882400 (1065)\ttotal: 6m 57s\tremaining: 56m 17s\n",
      "1200:\tlearn: 0.8942756\ttest: 0.8883800\tbest: 0.8885000 (1182)\ttotal: 7m 36s\tremaining: 55m 42s\n",
      "1300:\tlearn: 0.8951889\ttest: 0.8886600\tbest: 0.8886600 (1271)\ttotal: 8m 14s\tremaining: 55m 7s\n",
      "1400:\tlearn: 0.8964667\ttest: 0.8888800\tbest: 0.8889400 (1395)\ttotal: 8m 53s\tremaining: 54m 33s\n",
      "1500:\tlearn: 0.8976578\ttest: 0.8889400\tbest: 0.8890600 (1454)\ttotal: 9m 31s\tremaining: 53m 58s\n",
      "1600:\tlearn: 0.8986756\ttest: 0.8890400\tbest: 0.8890800 (1516)\ttotal: 10m 10s\tremaining: 53m 22s\n",
      "1700:\tlearn: 0.8999044\ttest: 0.8889600\tbest: 0.8890800 (1516)\ttotal: 10m 49s\tremaining: 52m 47s\n",
      "1800:\tlearn: 0.9010089\ttest: 0.8888600\tbest: 0.8890800 (1516)\ttotal: 11m 28s\tremaining: 52m 12s\n",
      "1900:\tlearn: 0.9021533\ttest: 0.8891200\tbest: 0.8891800 (1881)\ttotal: 12m 6s\tremaining: 51m 36s\n",
      "2000:\tlearn: 0.9031333\ttest: 0.8892400\tbest: 0.8893000 (1912)\ttotal: 12m 45s\tremaining: 51m\n",
      "2100:\tlearn: 0.9042733\ttest: 0.8893400\tbest: 0.8893800 (2078)\ttotal: 13m 25s\tremaining: 50m 28s\n",
      "2200:\tlearn: 0.9054244\ttest: 0.8894200\tbest: 0.8894400 (2193)\ttotal: 14m 4s\tremaining: 49m 51s\n",
      "2300:\tlearn: 0.9065844\ttest: 0.8894800\tbest: 0.8895200 (2205)\ttotal: 14m 43s\tremaining: 49m 14s\n",
      "2400:\tlearn: 0.9077289\ttest: 0.8896600\tbest: 0.8897200 (2380)\ttotal: 15m 21s\tremaining: 48m 37s\n",
      "2500:\tlearn: 0.9088156\ttest: 0.8898000\tbest: 0.8898400 (2410)\ttotal: 16m\tremaining: 48m 1s\n",
      "2600:\tlearn: 0.9099733\ttest: 0.8897200\tbest: 0.8898400 (2410)\ttotal: 16m 39s\tremaining: 47m 24s\n",
      "2700:\tlearn: 0.9111289\ttest: 0.8895600\tbest: 0.8898400 (2410)\ttotal: 17m 18s\tremaining: 46m 47s\n",
      "2800:\tlearn: 0.9122956\ttest: 0.8896200\tbest: 0.8898400 (2410)\ttotal: 17m 58s\tremaining: 46m 10s\n",
      "2900:\tlearn: 0.9133533\ttest: 0.8896200\tbest: 0.8898400 (2410)\ttotal: 18m 37s\tremaining: 45m 34s\n",
      "3000:\tlearn: 0.9145711\ttest: 0.8896000\tbest: 0.8898400 (2410)\ttotal: 19m 19s\tremaining: 45m 3s\n",
      "3100:\tlearn: 0.9157333\ttest: 0.8896000\tbest: 0.8898400 (2410)\ttotal: 20m\tremaining: 44m 30s\n",
      "3200:\tlearn: 0.9169711\ttest: 0.8896400\tbest: 0.8898400 (2410)\ttotal: 20m 41s\tremaining: 43m 57s\n",
      "3300:\tlearn: 0.9181867\ttest: 0.8899400\tbest: 0.8900200 (3297)\ttotal: 21m 22s\tremaining: 43m 23s\n",
      "3400:\tlearn: 0.9193356\ttest: 0.8899000\tbest: 0.8901000 (3369)\ttotal: 22m 4s\tremaining: 42m 49s\n",
      "3500:\tlearn: 0.9203733\ttest: 0.8899600\tbest: 0.8901000 (3369)\ttotal: 22m 45s\tremaining: 42m 15s\n",
      "3600:\tlearn: 0.9213578\ttest: 0.8899000\tbest: 0.8901000 (3369)\ttotal: 23m 28s\tremaining: 41m 42s\n",
      "3700:\tlearn: 0.9224156\ttest: 0.8898600\tbest: 0.8901000 (3369)\ttotal: 24m 11s\tremaining: 41m 9s\n",
      "3800:\tlearn: 0.9235356\ttest: 0.8900600\tbest: 0.8901000 (3369)\ttotal: 24m 54s\tremaining: 40m 37s\n",
      "3900:\tlearn: 0.9245822\ttest: 0.8901600\tbest: 0.8901600 (3899)\ttotal: 25m 37s\tremaining: 40m 3s\n",
      "4000:\tlearn: 0.9256089\ttest: 0.8901000\tbest: 0.8901800 (3901)\ttotal: 26m 18s\tremaining: 39m 26s\n",
      "4100:\tlearn: 0.9268444\ttest: 0.8902800\tbest: 0.8902800 (4100)\ttotal: 27m 1s\tremaining: 38m 51s\n",
      "4200:\tlearn: 0.9280133\ttest: 0.8900400\tbest: 0.8902800 (4100)\ttotal: 27m 42s\tremaining: 38m 14s\n",
      "4300:\tlearn: 0.9291356\ttest: 0.8899000\tbest: 0.8902800 (4100)\ttotal: 28m 21s\tremaining: 37m 33s\n",
      "4400:\tlearn: 0.9301244\ttest: 0.8900400\tbest: 0.8902800 (4100)\ttotal: 29m\tremaining: 36m 53s\n",
      "4500:\tlearn: 0.9310733\ttest: 0.8898800\tbest: 0.8902800 (4100)\ttotal: 29m 39s\tremaining: 36m 13s\n",
      "4600:\tlearn: 0.9320422\ttest: 0.8899200\tbest: 0.8902800 (4100)\ttotal: 30m 18s\tremaining: 35m 33s\n",
      "4700:\tlearn: 0.9329244\ttest: 0.8897400\tbest: 0.8902800 (4100)\ttotal: 30m 57s\tremaining: 34m 53s\n",
      "4800:\tlearn: 0.9340089\ttest: 0.8896800\tbest: 0.8902800 (4100)\ttotal: 31m 36s\tremaining: 34m 13s\n",
      "4900:\tlearn: 0.9349956\ttest: 0.8896800\tbest: 0.8902800 (4100)\ttotal: 32m 15s\tremaining: 33m 33s\n",
      "5000:\tlearn: 0.9358822\ttest: 0.8896400\tbest: 0.8902800 (4100)\ttotal: 32m 54s\tremaining: 32m 53s\n",
      "5100:\tlearn: 0.9366778\ttest: 0.8897200\tbest: 0.8902800 (4100)\ttotal: 33m 33s\tremaining: 32m 14s\n",
      "bestTest = 0.89028\n",
      "bestIteration = 4100\n",
      "Shrink model to first 4101 iterations.\n",
      "第9个子模型 accuracy0.89028\n",
      "9\n",
      "训练\n",
      "0:\tlearn: 0.8609156\ttest: 0.8577400\tbest: 0.8577400 (0)\ttotal: 479ms\tremaining: 1h 19m 52s\n",
      "100:\tlearn: 0.8764067\ttest: 0.8728000\tbest: 0.8729800 (96)\ttotal: 38.5s\tremaining: 1h 2m 53s\n",
      "200:\tlearn: 0.8810667\ttest: 0.8776600\tbest: 0.8778400 (196)\ttotal: 1m 16s\tremaining: 1h 2m 6s\n",
      "300:\tlearn: 0.8844667\ttest: 0.8811400\tbest: 0.8811400 (300)\ttotal: 1m 54s\tremaining: 1h 1m 28s\n",
      "400:\tlearn: 0.8857333\ttest: 0.8823000\tbest: 0.8823400 (391)\ttotal: 2m 32s\tremaining: 1h 48s\n",
      "500:\tlearn: 0.8871822\ttest: 0.8835600\tbest: 0.8836000 (496)\ttotal: 3m 10s\tremaining: 1h 8s\n",
      "600:\tlearn: 0.8887511\ttest: 0.8848400\tbest: 0.8848400 (583)\ttotal: 3m 48s\tremaining: 59m 32s\n",
      "700:\tlearn: 0.8898089\ttest: 0.8856800\tbest: 0.8856800 (700)\ttotal: 4m 26s\tremaining: 58m 55s\n",
      "800:\tlearn: 0.8906489\ttest: 0.8861200\tbest: 0.8861200 (800)\ttotal: 5m 4s\tremaining: 58m 18s\n",
      "900:\tlearn: 0.8916000\ttest: 0.8864600\tbest: 0.8864800 (894)\ttotal: 5m 43s\tremaining: 57m 45s\n",
      "1000:\tlearn: 0.8924067\ttest: 0.8867600\tbest: 0.8868000 (933)\ttotal: 6m 21s\tremaining: 57m 10s\n",
      "1100:\tlearn: 0.8933200\ttest: 0.8871200\tbest: 0.8871600 (1080)\ttotal: 7m\tremaining: 56m 39s\n",
      "1200:\tlearn: 0.8941578\ttest: 0.8873800\tbest: 0.8874400 (1192)\ttotal: 7m 39s\tremaining: 56m 4s\n",
      "1300:\tlearn: 0.8953311\ttest: 0.8875400\tbest: 0.8876400 (1291)\ttotal: 8m 18s\tremaining: 55m 29s\n",
      "1400:\tlearn: 0.8965133\ttest: 0.8877200\tbest: 0.8877400 (1383)\ttotal: 8m 56s\tremaining: 54m 55s\n",
      "1500:\tlearn: 0.8976778\ttest: 0.8879200\tbest: 0.8879800 (1496)\ttotal: 9m 35s\tremaining: 54m 19s\n",
      "1600:\tlearn: 0.8988111\ttest: 0.8880000\tbest: 0.8880600 (1572)\ttotal: 10m 14s\tremaining: 53m 44s\n",
      "1700:\tlearn: 0.8999222\ttest: 0.8879200\tbest: 0.8880600 (1572)\ttotal: 10m 53s\tremaining: 53m 9s\n",
      "1800:\tlearn: 0.9010311\ttest: 0.8879800\tbest: 0.8880600 (1572)\ttotal: 11m 32s\tremaining: 52m 34s\n",
      "1900:\tlearn: 0.9021956\ttest: 0.8882800\tbest: 0.8882800 (1900)\ttotal: 12m 12s\tremaining: 51m 58s\n",
      "2000:\tlearn: 0.9033889\ttest: 0.8882600\tbest: 0.8884600 (1923)\ttotal: 12m 51s\tremaining: 51m 23s\n",
      "2100:\tlearn: 0.9045178\ttest: 0.8883400\tbest: 0.8884800 (2047)\ttotal: 13m 31s\tremaining: 50m 51s\n",
      "2200:\tlearn: 0.9056711\ttest: 0.8884000\tbest: 0.8885400 (2166)\ttotal: 14m 10s\tremaining: 50m 14s\n",
      "2300:\tlearn: 0.9067733\ttest: 0.8884200\tbest: 0.8885400 (2166)\ttotal: 14m 49s\tremaining: 49m 36s\n",
      "2400:\tlearn: 0.9080689\ttest: 0.8880800\tbest: 0.8885400 (2166)\ttotal: 15m 28s\tremaining: 48m 59s\n",
      "2500:\tlearn: 0.9091289\ttest: 0.8880200\tbest: 0.8885400 (2166)\ttotal: 16m 8s\tremaining: 48m 22s\n",
      "2600:\tlearn: 0.9102800\ttest: 0.8881200\tbest: 0.8885400 (2166)\ttotal: 16m 47s\tremaining: 47m 45s\n",
      "2700:\tlearn: 0.9113711\ttest: 0.8879200\tbest: 0.8885400 (2166)\ttotal: 17m 26s\tremaining: 47m 7s\n",
      "2800:\tlearn: 0.9124467\ttest: 0.8880600\tbest: 0.8885400 (2166)\ttotal: 18m 5s\tremaining: 46m 29s\n",
      "2900:\tlearn: 0.9136933\ttest: 0.8882000\tbest: 0.8885400 (2166)\ttotal: 18m 44s\tremaining: 45m 52s\n",
      "3000:\tlearn: 0.9148244\ttest: 0.8881600\tbest: 0.8885400 (2166)\ttotal: 19m 23s\tremaining: 45m 14s\n",
      "3100:\tlearn: 0.9160133\ttest: 0.8883200\tbest: 0.8885400 (2166)\ttotal: 20m 3s\tremaining: 44m 36s\n",
      "bestTest = 0.88854\n",
      "bestIteration = 2166\n",
      "Shrink model to first 2167 iterations.\n",
      "第10个子模型 accuracy0.88854\n"
     ]
    }
   ],
   "source": [
    "# catboot\n",
    "from catboost import CatBoostClassifier\n",
    "X = features.drop(['timestamp'], axis=1)\n",
    "y = train['label']\n",
    "cat_model = CatBoostClassifier(loss_function=\"Logloss\",\n",
    "                                eval_metric=\"Accuracy\",\n",
    "                                task_type=\"GPU\",\n",
    "                                learning_rate=0.005,\n",
    "                                iterations=10000,\n",
    "                                random_seed=2019,\n",
    "                                od_type=\"Iter\",\n",
    "                                metric_period=10,\n",
    "                                depth=15,\n",
    "                                early_stopping_rounds=1000,\n",
    "                                use_best_model=True,\n",
    "                                bagging_temperature=0.7,\n",
    "                                leaf_estimation_method=\"Newton\",\n",
    "                                )\n",
    "skf = StratifiedKFold(n_splits=10, shuffle=True, random_state=2019)\n",
    "cat_prob = []\n",
    "cat_mean_acc = 0\n",
    "for k,(train_index, test_index) in enumerate(skf.split(X, y)):\n",
    "    print(k)\n",
    "    X_train, X_val = X.iloc[train_index], X.iloc[test_index]\n",
    "    y_train, y_val = y.iloc[train_index], y.iloc[test_index]\n",
    "    print('训练')\n",
    "    # 训练\n",
    "    cat = cat_model.fit(\n",
    "            X_train,\n",
    "            y_train,\n",
    "            eval_set=(X_val, y_val),\n",
    "            verbose=100,\n",
    "        )\n",
    "    \n",
    "    val_y_pred=cat_model.predict(X_val)\n",
    "    acc_val=accuracy_score(y_val,val_y_pred)\n",
    "    print('第{}个子模型 accuracy{}'.format(k+1,acc_val))\n",
    "    # 正式预测\n",
    "    test_y_pred=cat_model.predict_proba(test_features.drop(['timestamp'], axis=1))\n",
    "    cat_prob.append(test_y_pred)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cat结果\n",
    "new_result = (cat_prob[0]+cat_prob[1]+cat_prob[2]+cat_prob[3]+cat_prob[4]+cat_prob[5]+cat_prob[6]+cat_prob[7]+cat_prob[8]+cat_prob[9])/10\n",
    "final_result = []\n",
    "for i in new_result:\n",
    "    if i[0]>0.5:\n",
    "        final_result.append(0)\n",
    "    else:\n",
    "        final_result.append(1)\n",
    "final_result "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
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      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# xgb结果\n",
    "new_result = (xgb_prob[0]+xgb_prob[1]+xgb_prob[2]+xgb_prob[3]+xgb_prob[4])/5\n",
    "final_result = []\n",
    "for i in new_result:\n",
    "    if i[0]>0.5:\n",
    "        final_result.append(0)\n",
    "    else:\n",
    "        final_result.append(1)\n",
    "final_result "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lgb结果\n",
    "new_result = (prob[0]+prob[1]+prob[2]+prob[3]+prob[4])/5\n",
    "final_result = []\n",
    "for i in new_result:\n",
    "    if i[0]>0.5:\n",
    "        final_result.append(0)\n",
    "    else:\n",
    "        final_result.append(1)\n",
    "final_result "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
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      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# 融合结果\n",
    "xgb_result = (xgb_prob[0]+xgb_prob[1]+xgb_prob[2]+xgb_prob[3]+xgb_prob[4])/5\n",
    "lgb_result = (prob[0]+prob[1]+prob[2]+prob[3]+prob[4])/5\n",
    "result = (xgb_result+lgb_result)/2\n",
    "# 融合结果\n",
    "final_result = []\n",
    "for i in range(len(result)):\n",
    "\n",
    "    score = result[i][0]\n",
    "    if score >0.5:\n",
    "        final_result.append(0)\n",
    "    else:\n",
    "        final_result.append(1)\n",
    "final_result "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>150000 rows × 2 columns</p>\n",
       "</div>"
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      "text/plain": [
       "            sid  label\n",
       "0       1440682      0\n",
       "1       1606824      1\n",
       "2       1774642      0\n",
       "3       1742535      0\n",
       "4       1689686      1\n",
       "...         ...    ...\n",
       "149995  1165373      1\n",
       "149996  1444115      1\n",
       "149997  1134378      1\n",
       "149998  1700238      1\n",
       "149999  1201539      1\n",
       "\n",
       "[150000 rows x 2 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#features['version'].value_counts()\n",
    "res = pd.DataFrame(test['sid'])\n",
    "res['label'] = final_result\n",
    "res.to_csv('./result/cat_20211231_2.csv', index=False)\n",
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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